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Mahmood, Muhammad Tariq. "Face Detection by Image Discriminating." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4352.
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Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images. Some methods are exhibiting good results in certain conditions and others are good with different kinds of images. Image discriminating techniques are widely used for pattern and image analysis. Common discriminating methods are discussed.SIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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McIntyre, A. H. "Applying psychology to forensic facial identification : perception and identification of facial composite images and facial image comparison." Thesis, University of Stirling, 2012. http://hdl.handle.net/1893/9077.
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Eyewitness recognition is acknowledged to be prone to error but there is less understanding of difficulty in discriminating unfamiliar faces. This thesis examined the effects of face perception on identification of facial composites, and on unfamiliar face image comparison. Facial composites depict face memories by reconstructing features and configurations to form a likeness. They are generally reconstructed from an unfamiliar face memory, and will be unavoidably flawed. Identification will require perception of any accurate features, by someone who is familiar with the suspect and performance is typically poor. In typical face perception, face images are processed efficiently as complete units of information. Chapter 2 explored the possibility that holistic processing of inaccurate composite configurations will impair identification of individual features. Composites were split below the eyes and misaligned to impair holistic analysis (cf. Young, Hellawell, & Jay, 1987); identification was significantly enhanced, indicating that perceptual expertise with inaccurate configurations exerts powerful effects that can be reduced by enabling featural analysis. Facial composite recognition is difficult, which means that perception and judgement will be influence by an affective recognition bias: smiles enhance perceived familiarity, while negative expressions produce the opposite effect. In applied use, facial composites are generally produced from unpleasant memories and will convey negative expression; affective bias will, therefore, be important for facial composite recognition. Chapter 3 explored the effect of positive expression on composite identification: composite expressions were enhanced, and positive affect significantly increased identification. Affective quality rather than expression strength mediated the effect, with subtle manipulations being very effective. Facial image comparison (FIC) involves discrimination of two or more face images. Accuracy in unfamiliar face matching is typically in the region of 70%, and as discrimination is difficult, may be influenced by affective bias. Chapter 4 explored the smiling face effect in unfamiliar face matching. When multiple items were compared, positive affect did not enhance performance and false positive identification increased. With a delayed matching procedure, identification was not enhanced but in contrast to face recognition and simultaneous matching, positive affect improved rejection of foil images. Distinctive faces are easier to discriminate. Chapter 5 evaluated a systematic caricature transformation as a means to increase distinctiveness and enhance discrimination of unfamiliar faces. Identification of matching face images did not improve, but successful rejection of non-matching items was significantly enhanced. Chapter 6 used face matching to explore the basis of own race bias in face perception. Other race faces were manipulated to show own race facial variation, and own race faces to show African American facial variation. When multiple face images were matched simultaneously, the transformation impaired performance for all of the images; but when images were individually matched, the transformation improved perception of other race faces and discrimination of own race faces declined. Transformation of Japanese faces to show own race dimensions produced the same pattern of effects but failed to reach significance. The results provide support for both perceptual expertise and featural processing theories of own race bias. Results are interpreted with reference to face perception theories; implications for application and future study are discussed.
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Wysocki, Bruna. "Interação face a face: um estudo das estratégias discursivas na reconstrução da imagem." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/8/8142/tde-05052008-153601/.
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O presente trabalho tem por proposta analisar o discurso formulado por um expresidente que pretende reconstruir sua imagem abalada em decorrência do impeachment que sofreu. Ao considerarmos um encontro social, em que os interlocutores interagem face a face, observamos que o interactante procura articular estratégias interacionais, a fim de preservar sua face e protegê-la de eventuais ameaças; ao mesmo tempo, coordena estratégias argumentativas com o intuito de interferir na concepção inicial que seus interlocutores possuem a respeito de sua imagem. Para atingirmos nossos objetivos, consideramos, da Sociolingüística Interacional, as teorias de preservação de faces abordadas por Goffman (1974) e, ao levarmos em conta que estratégias argumentativas também colaboram para a reconstrução da imagem, recorremos aos pressupostos da Teoria da Argumentação, segundo estudos de Perelman e Olbrechts-Tyteca (2002). Com base nesses estudos, partimos para a aplicação dos conceitos em um corpus constituído por uma entrevista televisiva, transmitida pelo Sistema Brasileiro de Televisão (SBT), em agosto de 1998, no \"Programa Livre\", em que o ex-presidente Fernando Collor de Melo é entrevistado por estudantes de ensino médio e cursinho. O corpus foi gravado e transcrito de acordo com as normas publicadas pelo Projeto da Norma Urbana Culta - NURC-SP.This paper has the purpose of analyzing the discourse delivered by a former president that plans to rebuild his image, since it was shattered as a result of an impeachment sustained by him. Upon considering a social gathering, in which the interlocutors interact face to face, we have noted that the interacting person tries to coordinate interactional strategies in order to preserve his face and protect it against any threats; at the same time, he organizes strategic arguments for the purpose of interfering with the initial assumption that his interlocutors have made in regard to his image. To achieve our goals, we have used, from the Interactional Sociolinguistics, the theory of faces\' preservation as approached by Goffman (1974) and, by taking into account that strategic arguments too cooperate for the reconstruction of the image, we have relied on the assumptions of the Argumentation Theory, according to studies by Perelman & Olbrechts-Tyteca (2002). With basis on this information, we set out to apply the concepts in a corpus made up by a television interview broadcast by SBT, a television network, in August 1998, in the \"Programa Livre\" talk show, in which former President Fernando Collor de Melo was interviewed by high school students. The corpus was recorded and transcribed according to the rules published by Urban Educated Norm Project - NURC - SP
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PINHO, JOSÉ LUIZ BUONOMO DE. "IMAGE QUALITY METRICS FOR FACE RECOGNITION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=22825@1.
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O Reconhecimento Facial é o processo de identificação de uma pessoa a
partir da imagem de sua face. Na forma mais usual, o processo de identificação
consiste em extrair informações dessa imagem e compará-las com informações
relativas a outras imagens armazenadas numa base de dados e por fim indicar na
saída a imagem da base mais similar à imagem de entrada. O desempenho desse
processo está diretamente ligado à qualidade das imagens, tanto das que estão
armazenadas na base de dados, quanto da imagem do indivíduo cuja identidade
está sendo determinada. Por isso, convém que a qualidade das imagens faciais seja
avaliada antes que estas sejam submetidas ao procedimento de reconhecimento.
A maioria dos métodos apresentados até o momento na literatura baseia-se em um
conjunto de critérios, cada um voltado a um atributo isolado da imagem. A
qualidade da imagem é considerada adequada se aprovada por todos os critérios
individualmente. Desconsidera-se, portanto, o efeito cumulativo de diversos
fatores que afetam a qualidade das imagens e, por conseguinte, o desempenho do
reconhecimento facial. Essa monografia propõe uma metodologia para o projeto
de métricas de qualidade de imagens faciais que expressem num único índice o
efeito combinado de diversos fatores que afetam o reconhecimento. Tal índice é
dado por uma função de um conjunto de atributos extraídos diretamente da
imagem. O presente estudo analisa experimentalmente uma função linear e uma
rede neural do tipo back-propagation como alternativas para a estimativa de
qualidade a partir dos atributos. Experimentos conduzidos sobre a base de dados
IMM para o algoritmo de reconhecimento baseado em padrões binários locais
comprovam a o bom desempenho da metodologia.Face Recognition is the process of identifying people based on facial images. In its most usual form the identification procedure consists of extracting information from an input face image and comparing them to the records of other face images stored in a face data base, and finally indicating the most similar one to the input image. The performance of this process is directly dependent on the input image quality, as well as on the images in the data base. Thus, it is important that the quality of a face image is tested before it is given to the recognition procedure, either as a input image or as a new record in the face database. Most methods proposed thus far based on a set of criteria, each one devoted to an isolated attribute. The image quality is considered adequate if approved by all criteria individually. Thus, the cumulative effect of different factors affecting the image quality is no regarded. This dissertation proposes a methodology for the design of quality metrics of facial images that Express in a single scalar the combined effect of multiple factors affecting the quality. Such score is given by a function of attributes extracted directly from the image. This study investigates a linear and a non-linear approach for quality assessment. Experiments conducted upon the IMM face database for a Local Binary Pattern face recognition algorithm demonstrate the good performance of the proposed methodology.
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Mutelo, Risco Mulwani. "Biometric face image representation and recognition." Thesis, University of Newcastle upon Tyne, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548004.
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Ribeiro, Ricardo Ferreira. "Face detection on infrared thermal image." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23551.
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Mestrado em Engenharia Eletrónica e TelecomunicaçõesInfrared cameras or thermal imaging cameras are devices that use infrared radiation to capture an image. This kind of sensors are being developed for almost a century now. They started to be used in the military environment, but at that time it took too long to create a single image. Nowadays, the infrared sensors have reached a whole new technological level and are used for other than military purposes. These sensors are being used for face detection in this thesis. When comparing the use of thermal images regarding color images, it is possible to see advantages and limitations, such as capture images in total darkness and high price, respectively, which will be explored throughout this document. This work proposes the development or adaptation of several methods for face detection on infrared thermal images. The well known algorithm developed by Paul Viola and Michael Jones, using Haar feature-based cascade classi ers, is used to compare the traditional algorithms developed for visible light images when applied to thermal imaging. Three di erent algorithms for face detection are presented. Face segmentation is the rst step in these methods. A method for the segmentation and ltering of the face in the infrared thermal images resulting in a binary image is proposed. In the rst method, an edge detection algorithm is applied to the binary image and the face detection is based on these contours. In the second method, a template matching method is used for searching and nding the location of a template image with the shape of a human head in the binary image. In the last one, a matching algorithm is used. This algorithm correlates a template with the distance transform of the edge image. This algorithm incorporates edge orientation information resulting in the reduction of false detection and the cost variation is limited. The experimental results show that the proposed methods have promising outcome, but the second method is the most suitable for the performed experiments.
As camaras infravermelhas ou as camaras de imagem termica sao dispositivos que usam radiação infravermelha para capturar uma imagem. Este tipo de sensores estao a ser desenvolvidos há quase um século. Começaram a ser usados para fins militares, mas naquela época demorava demasiado tempo para criar uma única imagem. Hoje em dia, os sensores infravermelhos alcançaram um nível tecnológico totalmente novo e são usados para fins além de militares. Esses sensores estão ser usados para detecção facial nesta dissertação. Comparando o uso de imagens térmicas relativamente a imagens coloridas, é possível ver vantagens e limitações, tal como a captura de imagens na escuridão e o preço elevado, respectivamente, que serão exploradas durante este documento. Este trabalho propõe o desenvolvimento ou adaptação de vários métodos para a detecção facial em imagens térmicas. O conhecido algoritmo desenvolvido por Paul Viola e Michael Jones, que utiliza cascatas de classificadores de Haar baseado em características, é usado para comparar os algoritmos tradicionais desenvolvidos para imagens de luz visível quando aplicados a imagens térmicas. São apresentados três métodos diferentes para a detecção facial. A segmentação do rosto e o primeiro passo nestes métodos. E proposto um método para a segmentação e filtragem do rosto nas imagens térmicas que tem como resultado uma imagem binária. No primeiro método, é aplicado um algoritmo de detecção de contornos a imagem binária e a detecção facial é baseada nesses contornos. No segundo método, é usado um método de correspondência de padrões para pesquisar e encontrar a localização de uma imagem padrão com a forma da cabeça humana na imagem binária. No último, é usado um algoritmo de correspondência. Este algoritmo correlaciona um padrão com a transformada de distância da imagem de contornos. Este algoritmo incorpora informações de orientação de contornos que resulta na redução de falsas detecções e a variação do custo é limitada. Os resultados experimentais mostram que os métodos propostos têm resultados promissores, mas o segundo método é o mais adequado para as experiências realizadas.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.
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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Ebrahimpour-Komleh, Hossein. "Fractal techniques for face recognition." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16289/.
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Fractals are popular because of their ability to create complex images using only several simple codes. This is possible by capturing image redundancy and presenting the image in compressed form using the self similarity feature. For many years fractals were used for image compression. In the last few years they have also been used for face recognition. In this research we present new fractal methods for recognition, especially human face recognition.
This research introduces three new methods for using fractals for face recognition, the use of fractal codes directly as features, Fractal image-set coding and Subfractals. In the first part, the mathematical principle behind the application of fractal image codes for recognition is investigated. An image Xf can be represented as Xf = A x Xf + B which A and B are fractal parameters of image Xf . Different fractal codes can be presented for any arbitrary image. With the defnition of a fractal transformation, T(X) = A(X - Xf ) + Xf , we can define the relationship between any image produced in the fractal decoding process starting with any arbitrary image X0 as Xn = Tn(X) = An(X - Xf ) + Xf . We show that some choices for A or B lead to faster convergence to the final image.
Fractal image-set coding is based on the fact that a fractal code of an arbitrary gray-scale image can be divided in two parts - geometrical parameters and luminance parameters. Because the fractal codes for an image are not unique, we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters
the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters - which are faster to compute. For recognition purposes, the fractal code of a query image is applied to all the images in the training set for one iteration. The distance between an image and the result after one iteration is used to define a similarity measure between this image and the query image.
The fractal code of an image is a set of contractive mappings each of which transfer a domain block to its corresponding range block. The distribution of selected domain blocks for range blocks in an image depends on the content of image and the fractal encoding algorithm used for coding. A small variation in a part of the input image may change the contents of the range and domain blocks in the fractal encoding process, resulting in a change in the transformation parameters in the same part or even other parts of the image. A subfractal is a set of fractal codes related to range blocks of a part of the image. These codes are calculated to be independent of other codes of the other parts of the same image. In this case the domain blocks nominated for each range block must be located in the same part of the image which the range blocks come from.
The proposed fractal techniques were applied to face recognition using the MIT and XM2VTS face databases. Accuracies of 95% were obtained with up to 156 images.
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Lee, Jinho. "Synthesis and analysis of human faces using multi-view, multi-illumination image ensembles." Columbus, Ohio : Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1133366279.
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Broderick, Shawn D. "A Comparison of Mathematical Discourse in Online and Face-to-Face Environments." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2820.pdf.
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Bryan, Angela E. "Face-to-face communication in Australian workplaces : a social rules approach /." St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16510.pdf.
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Bilson, Amy Jo. "Image size and resolution in face recognition /." Thesis, Connect to this title online; UW restricted, 1987. http://hdl.handle.net/1773/9166.
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Duffner, Stefan. "Face image analysis with convolutional neural networks." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:25-opus-48350.
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Omar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.
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Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques.
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Feng, Guo Can. "Face recognition using virtual frontal-view image." HKBU Institutional Repository, 1999. http://repository.hkbu.edu.hk/etd_ra/267.
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Kripakaran, Rolance. "Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5705.
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The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.
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Nyland, Robert Scott. "The Gratification Niches of Internet Social Networking, E-mail, and Face-to-face Communication." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2151.pdf.
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Moore, Thomas Brendan. "Learning Geometry-Free Face Re-lighting." Master's thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3353.
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The accurate modeling of the variability of illumination in a class of images is a fundamental problem that occurs in many areas of computer vision and graphics. For instance, in computer vision there is the problem of facial recognition. Simply, one would hope to be able to identify a known face under any illumination. On the other hand, in graphics one could imagine a system that, given an image, the illumination model could be identified and then used to create new images. In this thesis we describe a method for learning the illumination model for a class of images. Once the model is learnt it is then used to render new images of the same class under the new illumination. Results are shown for both synthetic and real images. The key contribution of this work is that images of known objects can be re-illuminated using small patches of image data and relatively simple kernel regression models. Additionally, our approach does not require any knowledge of the geometry of the class of objects under consideration making it relatively straightforward to implement. As part of this work we will examine existing geometric and image-based re-lighting techniques; give a detailed description of our geometry-free face re-lighting process; present non-linear regression and basis selection with respect to image synthesis; discuss system limitations; and look at possible extensions and future work.M.S.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science MS
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Pereira, Diogo Camara. "Face recognition using infrared imaging." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Dec%5FPereira.pdf.
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Thesis (Electrical Engineer and M.S. in Electrical Engineering)--Naval Postgraduate School, December 2002.Thesis advisor(s): Monique P. Fargues, Gamani Karunasiri, Roberto Cristi. Includes bibliographical references (p. 93-95). Also available online.
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Bartlett, Marian Stewart. "Face image analysis by unsupervised learning and redundancy reduction /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9907603.
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Akinbola, Akintunde A. "Estimation of image quality factors for face recognition." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4308.
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Thesis (M.S.)--West Virginia University, 2005.Title from document title page. Document formatted into pages; contains vi, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-56).
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Song, Qing. "Features and statistical classifiers for face image analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0035/NQ62459.pdf.
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CastelaÌn, Mario. "Face shape recovery from a single image view." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431579.
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Dasgupta, Sankarshan. "3D Face Reconstruction From Front And Profile Image." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1626689472561706.
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Li, Yang. "Face Recognition Based on Histogram And Spin Image." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485831.
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This thesis presents our research work on shape-based human face recognition exploiting curvature-based histogram and spin image. Instead of the popular 2D shape information represented by fiducial points, the novelty here is the use of 2.5D shape information obtained by shape-from-shading (SFS). Though surface normals generated by performing shape-from-shading on objects are not widely accepted as a precise shape representation for face recognition purposes, recent research in shape-from-shading [Ragheb and Hancock, 2003] [prados et aI., 2006] [Castelan, 2006] [Smith and Hancock, 2006] has made it possible to recover fairly accurate shape information under various conditions from face images in the real world. These contributions make our face recognition approaches based on 2.5D shape recovered from a single image possi.ble. Chapter 2 is a thorough review of the existing literature in the areas pf surface reconstruction using shape-from-shading, appearance-based and model-based face recognition on 2D and 3D data, and histogram-based image representation and recognition. The literature is pretty sparse on works on shape-from-shading in the face recognition area, however there are plenty of approaches based on 2D images ~ and 3D range data. With accurate height map being recovered from single face image [Prados et aI., 2006] [Castelan, 2006] and statistical model being proposed to recover surface normals [Smith and Hancock, 2006], we have enough 2.5D shape infonnation recovered' from 2D image based on which we can perfonn face recognition. In Chapter 3, we present our curvature-based histogram appro,ach as our first contribution, which employs principal curvature infonnation calculated from the Hessian matrix based on the recovered surface nonnals. Generalized entropies are introduced as similarity measurements, which give stable performance especially when the number of relative images varies. While curvature-based histogram proves to be a fairly good face recognition approach with concise representation and easy comparison, its performance is not always stable when applied to different databases. Therefore more advanced facerecognition approach is required to bring better and more stable identification results. In Chapter 4, we derive patch-based spin image as a local shape representation from the idea of global curvature-based histogram in Chapter 3. This representation is inspired by [Johnson and Hebert, 1999] and adapted in this thesis as a solution to face recognition problem. Instead of using 3D range data, the estimated height map reconstructed by shape-from-shading is employed in the spin image construction. Also the mean needle map model is used as the preprocessing to correct the errors and noise that exists in the surface nonnal estimates. Face surface is segmented into small patches and the spin image corresponding to the surface is composed of histograms constructed on each surface patch. In Appendix A, we propose dual spin image to address the difficulties of recognizing face in rotation, among which the two major problems of point correspondence and point occlusion are of particular importance. Therefore we propose the idea of neighbour area spin image to construct the pointwise neighbourhood surface feature collection and use the ratio of projected distances to relative angles to alleviate the errors introduced by surface rotation. We also present the linear model and the finite Gaussian mixture model to approximate novel dual spin image using the existing dual spin images. Face recognition is performed based on the model parameters. .The work in this thesis suggests that 2.5D shape feature recovered by shapefrom- shading can be used for the purpose offace recognition. Also the appearancebased approaches derived from histogram are effective for face recognition. The result suggests that shape information recovered from single image is sufficient for face recognition based on the condition that shape-from:-shading can successfully recover the surface normal fields and the height map from the image.
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Kafetzi, Evi. "L'Ethos dans l'Argumentation : le cas du face à face Sarkozy / Royal 2007." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0053/document.
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En quête d'efficacité et d'influence, tout candidat aux électionsprésidentielles tente de se fabriquer et de donner à voir une image de soi conformeaux attentes des électeurs concernant le profil d'un futur chef d'État. Cette imagede soi séduisante construite à travers le discours, appelée ethos en rhétorique, faitpartie intégrante de l'argumentation au même titre que ses autres composantes, àsavoir le logos et le pathos. Le discours politique, en tant que porteur d'importantsenjeux, est le terrain de construction identitaire par excellence.Ce travail explore les stratégies communicatives dans l'activitéargumentative qu'est le débat politique télévisé. Les données sont constituées parle face à face télévisé du 2 mai 2007 entre Nicolas Sarkozy et Ségolène Royal, àla veille du deuxième tour des élections présidentielles françaises.Je me propose dans ce travail de dégager les règles et les mécanismes surlesquels repose la fabrication d'une image de soi télévisuelle par les praticiens dela persuasion que sont les hommes et les femmes politiques, afin de parvenir àleurs fins. Les outils langagiers dont les deux adversaires se servent lors du dueltélévisé en question ici, pour nous servir une image de soi conforme au modèleprésidentiel « idéal » sont analysés un par un. Ainsi, ayant une meilleureconnaissance des coulisses de la rhétorique audiovisuelle, l'électeur-téléspectateurdevient maître de sa décision et responsable de son choix, et apprend à se méfierdes sentiments et des impressions que lui inspirent les praticiens de la persuasionIn search of effectiveness and influence, every candidate who stands forpresidential elections attempts to create and give to the audience a self-imageconsistent with the elector's expectations concerning a future head of state'sprofile. This attractive self-image created through discourse, called ethos inrhetoric, is an integral part of argumentation, as well as its other components,logos and pathos. Political discourse, as a vector of important stakes, constitutesthe ground of identity construction par excellence.This work explores communication strategies in argumentation activity,and particularly in televised political debate. The data is constituted by thetelevised face to face of the 2nd of May 2007 between Nicolas Sarkozy andSégolène Royal, at the eve of the second ballot of the French presidentialelection.What I propose in this work is to draw up the rules and mechanisms thatgovern the making of one's televised self-image by politicians, spin doctors, inorder to achieve their ends. I propose to analyse, one by one, the linguistic toolsthat the two opponents use in order to give the audience a self-image consistentwith an ideal presidential model, during the televised duel that we're studyinghere. In this way, having a better knowledge of what goes on behind the scenesof audiovisual rhetoric, the elector-televiewer becomes master of his decisionand has the control of his choice and learns to beware of feelings andimpressions inspired by the professionals of persuasion
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Costen, Nicholas Paul. "Spatial frequencies and face recognition." Thesis, University of Aberdeen, 1994. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU069146.
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If face images are degraded by spatial quantisation there is a non-linear acceleration of the decline of recognition accuracy as block-size increases, suggesting recognition requires a critical minimum range of object spatial frequencies. These may define the facial configuration, reflecting the structural properties allowing differentiation of faces. Experiment 1 measured speed and accuracy of recognition of six fronto-parallel faces shown with 11, 21 and 42 pixels/face, produced by quantisation, a Fourier low-pass filter and Gaussian blurring. Performance declined with image quality in a significant, non-linear manner, but faster for the quantised images. Experiment 2 found some of this additional decline was due to frequency-domain masking. Experiment 3 compared recognition for quantised, Fourier low-pass and high-pass versions, recognition was only impaired when the frequency limit exceeded the range 4.5-12.5 cycles/face. Experiment 4 found this was not due to contrast differences. Experiments 5, 6 and 7 used octave band-pass filters centred on 4.14, 9.67 and 22.15 cycles/face, varying view-point for both sequential matching and recognition. The spatial frequency effect was not found for matching, but was for recognition. Experiment 8 also measured recognition of band-passed images, presented with octave bands centred on 2.46-50.15 cycles/face and at 0-90 degrees from fronto-parallel. Spatial frequency effects were found at all angles, with best performance for semi-profile images and 11.10 cycles/face. Experiment 9 replicated this, with perceptually equal contrasts and the outer facial contour removed. Modeling showed this reflected a single spatial-frequency channel two octaves wide, centred on 9 cycles/face. Experiment 10 measured response time for successive matching of faces across a size-disparity, finding an asymmetrical effect.
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Hinze, Carl Graham. "Re-thinking 'face' : pursuing an emic-etic understanding of Chinese Mian and Lian and English face /." St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16544.pdf.
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Du, Shan. "Image-based face recognition under varying pose and illuminations conditions." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2814.
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Image-based face recognition has attained wide applications during the past decades in commerce and law enforcement areas, such as mug shot database matching, identity authentication, and access control. Existing face recognition techniques (e.g., Eigenface, Fisherface, and Elastic Bunch Graph Matching, etc.), however, do not perform well when the following case inevitably exists. The case is that, due to some variations in imaging conditions, e.g., pose and illumination changes, face images of the same person often have different appearances. These variations make face recognition techniques much challenging. With this concern in mind, the objective of my research is to develop robust face recognition techniques against variations.
This thesis addresses two main variation problems in face recognition, i.e., pose and illumination variations. To improve the performance of face recognition systems, the following methods are proposed: (1) a face feature extraction and representation method using non-uniformly selected Gabor convolution features, (2) an illumination normalization method using adaptive region-based image enhancement for face recognition under variable illumination conditions, (3) an eye detection method in gray-scale face images under various illumination conditions, and (4) a virtual pose generation method for pose-invariant face recognition. The details of these proposed methods are explained in this thesis. In addition, we conduct a comprehensive survey of the existing face recognition methods. Future research directions are pointed out.
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Youmaran, Richard. "Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images." Thesis, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19729.
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Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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Lin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16703/.
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Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.
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Duffner, Stefan [Verfasser]. "Face Image Analysis with Convolutional Neural Networks / Stefan Duffner." München : GRIN Verlag, 2009. http://d-nb.info/1176815849/34.
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Olausson, Erik. "Face Recognition for Mobile Phone Applications." Thesis, Linköping University, Department of Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11850.
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Att applicera ansiktsigenkänning direkt på en mobiltelefon är en utmanande uppgift, inte minst med tanke på den begränsade minnes- och processorkapaciteten samt den stora variationen med avseende på ansiktsuttryck, hållning och ljusförhållande i inmatade bilder.
Det är fortfarande långt kvar till ett färdigutvecklat, robust och helautomatiskt ansiktsigenkänningssystem för den här miljön. Men resultaten i det här arbetet visar att genom att plocka ut feature-värden från lokala regioner samt applicera en välgjord warpstrategi för att minska problemen med variationer i position och rotation av huvudet, är det möjligt att uppnå rimliga och användbara igenkänningsnivåer. Speciellt för ett halvautomatiskt system där användaren har sista ordet om vem personen på bilden faktiskt är.
Med ett galleri bestående av 85 personer och endast en referensbild per person nådde systemet en igenkänningsgrad på 60% på en svårklassificerad serie testbilder. Totalt 73% av gångerna var den rätta individen inom de fyra främsta gissningarna.
Att lägga till extra referensbilder till galleriet höjer igenkänningsgraden rejält, till nästan 75% för helt korrekta gissningar och till 83,5% för topp fyra. Detta visar att en strategi där inmatade bilder läggs till som referensbilder i galleriet efterhand som de identifieras skulle löna sig ordentligt och göra systemet bättre efter hand likt en inlärningsprocess.
Detta exjobb belönades med pris för "Bästa industrirelevanta bidrag" vid Svenska sällskapet för automatiserad bildanalys årliga konferens i Lund, 13-14 mars 2008.
Applying face recognition directly on a mobile phone is a challenging proposal due to the unrestrained nature of input images and limitations in memory and processor capabilities.
A robust, fully automatic recognition system for this environment is still a far way off. However, results show that using local feature extraction and a warping scheme to reduce pose variation problems, it is possible to capitalize on high error tolerance and reach reasonable recognition rates, especially for a semi-automatic classification system where the user has the final say.
With a gallery of 85 individuals and only one gallery image per individual available the system is able to recognize close to 60 % of the faces in a very challenging test set, while the correct individual is in the top four guesses 73% of the time.
Adding extra reference images boosts performance to nearly 75% correct recognition and 83.5% in the top four guesses. This suggests a strategy where extra reference images are added one by one after correct classification, mimicking an online learning strategy.
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Lopes, Daniel Pedro Ferreira. "Face verication for an access control system in unconstrained environment." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23395.
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Mestrado em Engenharia Eletrónica e TelecomunicaçõesO reconhecimento facial tem vindo a receber bastante atenção ao longo dos últimos anos não só na comunidade cientifica, como também no ramo comercial. Uma das suas várias aplicações e o seu uso num controlo de acessos onde um indivíduo tem uma ou várias fotos associadas a um documento de identificação (também conhecido como verificação de identidade). Embora atualmente o estado da arte apresente muitos estudos em que tanto apresentam novos algoritmos de reconhecimento como melhorias aos já desenvolvidos, existem mesmo assim muitos problemas ligados a ambientes não controlados, a aquisição de imagem e a escolha dos algoritmos de deteção e de reconhecimento mais eficazes. Esta tese aborda um ambiente desafiador para a verificação facial: um cenário não controlado para o acesso a infraestruturas desportivas. Uma vez que não existem condições de iluminação controladas nem plano de fundo controlado, isto torna um cenário complicado para a implementação de um sistema de verificação facial. Esta tese apresenta um estudo sobre os mais importantes algoritmos de detecção e reconhecimento facial assim como técnicas de pré-processamento tais como o alinhamento facial, a igualização de histograma, com o objetivo de melhorar a performance dos mesmos. Também em são apresentados dois métodos para a aquisição de imagens envolvendo a seleção de imagens e calibração da câmara. São apresentados resultados experimentais detalhados baseados em duas bases de dados criadas especificamente para este estudo. No uso de técnicas de pré-processamento apresentadas, foi possível presenciar melhorias até 20% do desempenho dos algoritmos de reconhecimento referentes a verificação de identidade. Com os métodos apresentados para os testes ao ar livre, foram conseguidas melhorias na ordem dos 30%.
Face Recognition has been received great attention over the last years, not only on the research community, but also on the commercial side. One of the many uses of face recognition is its use on access control systems where a person has one or several photos associated to an Identi cation Document (also known as identity veri cation). Although there are many studies nowadays, both presenting new algorithms or just improvements of the already developed ones, there are still many open problems regarding face recognition in uncontrolled environments, from the image acquisition conditions to the choice of the most e ective detection and recognition algorithms, just to name a few. This thesis addresses a challenging environment for face veri cation: an unconstrained environment for sports infrastructures access. As there are no controlled lightning conditions nor controlled background, this makes a di cult scenario to implement a face veri cation system. This thesis presents a study of some of the most important facial detection and recognition algorithms as well as some pre-processing techniques, such as face alignment and histogram equalization, with the aim to improve their performance. It also introduces some methods for a more e cient image acquisition based on image selection and camera calibration, specially designed for addressing this problem. Detailed experimental results are presented based on two new databases created speci cally for this study. Using pre-processing techniques, it was possible to improve the recognition algorithms performances up to 20% regarding veri cation results. With the methods presented for the outdoor tests, performances had improvements up to 30%
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Bayar, Hakan. "Texture Mapping By Multi-image Blending For 3d Face Models." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609063/index.pdf.
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Computer interfaces has changed to 3D graphics environments due to its high
number of applications ranging from scientific importance to entertainment. To
enhance the realism of the 3D models, an established rendering technique, texture
mapping, is used. In computer vision, a way to generate this texture is to combine
extracted parts of multiple images of real objects and it is the topic studied in this
thesis. While the 3D face model is obtained by using 3D scanner, the texture to
cover the model is constructed from multiple images. After marking control points
on images and also on 3D face model, a texture image to cover the 3D face model
is generated. Moreover, effects of the some features of OpenGL, a graphical
library, on 3D texture covered face model are studied.
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Zheng, Yilin. "Text-Based Speech Video Synthesis from a Single Face Image." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788.
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Man, Chun Him. "Human face image searching system with relevance feedback using sketch." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/618.
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Ahonen, T. (Timo). "Face and texture image analysis with quantized filter response statistics." Doctoral thesis, University of Oulu, 2009. http://urn.fi/urn:isbn:9789514291821.
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Abstract
Image appearance descriptors are needed for different computer vision applications dealing with, for example, detection, recognition and classification of objects, textures, humans, etc. Typically, such descriptors should be discriminative to allow for making the distinction between different classes, yet still robust to intra-class variations due to imaging conditions, natural changes in appearance, noise, and other factors.
The purpose of this thesis is the development and analysis of photometric descriptors for the appearance of real life images. The two application areas included in this thesis are face recognition and texture classification.
To facilitate the development and analysis of descriptors, a general framework for image description using statistics of quantized filter bank responses modeling their joint distribution is introduced. Several texture and other image appearance descriptors, including the local binary pattern operator, can be presented using this model. This framework, within which the thesis is presented, enables experimental evaluation of the significance of each of the components of this three-part chain forming a descriptor from an input image.
The main contribution of this thesis is a face representation method using distributions of local binary patterns computed in local rectangular regions. An important factor of this contribution is to view feature extraction from a face image as a texture description problem. This representation is further developed into a more precise model by estimating local distributions based on kernel density estimation. Furthermore, a face recognition method tolerant to image blur using local phase quantization is presented.
The thesis presents three new approaches and extensions to texture analysis using quantized filter bank responses. The first two aim at increasing the robustness of the quantization process. The soft local binary pattern operator accomplishes this by making a soft quantization to several labels, whereas Bayesian local binary patterns make use of a prior distribution of labelings, and aim for the one maximizing the a posteriori probability. Third, a novel method for computing rotation invariant statistics from histograms of local binary pattern labels using the discrete Fourier transform is introduced.
All the presented methods have been experimentally validated using publicly available image datasets and the results of experiments are presented in the thesis. The face description approach proposed in this thesis has been validated in several external studies, and it has been utilized and further developed by several research groups working on face analysis.
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Wall, Helene. "Context-Based Algorithm for Face Detection." Thesis, Linköping University, Department of Science and Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4171.
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Face detection has been a research area for more than ten years. It is a complex problem due to the high variability in faces and amongst faces; therefore it is not possible to extract a general pattern to be used for detection. This is what makes the face detection problem a challenge.
This thesis gives the reader a background to the face detection problem, where the two main approaches of the problem are described. A face detection algorithm is implemented using a context-based method in combination with an evolving neural network. The algorithm consists of two majors steps: detect possible face areas and within these areas detect faces. This method makes it possible to reduce the search space.
The performance of the algorithm is evaluated and analysed. There are several parameters that affect the performance; the feature extraction method, the classifier and the images used.
This work resulted in a face detection algorithm and the performance of the algorithm is evaluated and analysed. The analysis of the problems that occurred has provided a deeper understanding for the complexity of the face detection problem.
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Shin, Joo-Heon. "TOPOLOGICAL PROPERTIES OF A NETWORK OF SPIKING NEURONS IN FACE IMAGE RECOGNITION." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2133.
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We introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The network performed satisfactorily given appropriate topology, i.e. the number of neurons and synaptic connections, which corresponded to the size of input images. Comparison of Synaptic Plasticity Activity Rule (SAPR) and Spike Timing Dependant Plasticity (STDP) rules, used to update connections between the neurons, indicated that the SAPR gave better results and thus was used throughout. Test results showed that the network performed better than Support Vector Machines. We also introduced a stopping criterion based on entropy, which significantly shortened the iterative process while only slightly affecting classification performance.
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Chen, Shaokang. "Robust discriminative principal component analysis for face recognition /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18934.pdf.
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Jones, Creed Farris III. "Color Face Recognition using Quaternionic Gabor Filters." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/26591.
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This dissertation reports the development of a technique for automated face recognition, using color images. One of the more powerful techniques for recognition of faces in monochromatic images has been extended to color by the use of hypercomplex numbers called quaternions. Two software implementations have been written of the new method and the analogous method for use on monochromatic images. Test results show that the new method is superior in accuracy to the analogous monochrome method.
Although color images are generally collected, the great majority of published research efforts and of commercially available systems use only the intensity features. This surprising fact provided motivation to the three thesis statements proposed in this dissertation.
The first is that the use of color information can increase face recognition accuracy. Face images contain many features, some of which are only easily distinguishable using color while others would seem more robust to illumination variation when color is considered.
The second thesis statement is that the currently popular technique of graph-based face analysis and matching of features extracted from application of a family of Gabor filters can be extended to use with color. A particular method of defining a filter appropriate for color images is used; the usual complex Gabor filter is adapted to the domain of quaternions.. Four alternative approaches to the extension of complex Gabor filters to quaternions are defined and discussed; the most promising is selected and used as the basis for subsequent implementation and experimentation.
The third thesis statement is that statistical analysis can identify portions of the face image that are highly relevant â i.e., locations that are especially well suited for use in face recognition systems. Conventionally, the Gabor-based graph method extracts features at locations that are equally spaced, or perhaps selected manually on a non-uniform graph. We have defined a relevance image, in which the intensity values are computed from the intensity variance across a number of images from different individuals and the mutual information between the pixel distributions of sets of images from different individuals and the same individual.
A complete software implementation of the new face recognition method has been developed. Feature vectors called jets are extracted by application of the novel quaternion Gabor filter, and matched against models of other faces. In order to test the validity of the thesis statements, a parallel software implementation of the conventional monochromatic Gabor graph method has been developed and side-by-side testing has been conducted. Testing results show accuracy increases of 3% to 17% in the new color-based method over the conventional monochromatic method. These testing results demonstrate that color information can indeed provide a significant increase in accuracy, that the extension of Gabor filters to color through the use of quaternions does give a viable feature set, and that the face landmarks chosen via statistical methods do have high relevance for face discrimination.Ph. D.
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Gunn, Steve R. "Dual active contour models for image feature extraction." Thesis, University of Southampton, 1996. https://eprints.soton.ac.uk/250089/.
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Active contours are now a very popular technique for shape extraction, achieved by minimising a suitably formulated energy functional. Conventional active contour formulations suffer difficulty in appropriate choice of an initial contour and values of parameters. Recent approaches have aimed to resolve these problems, but can compromise other performance aspects. To relieve the problem in initialisation, an evolutionary dual active contour has been developed, which is combined with a local shape model to improve the parameterisation. One contour expands from inside the target feature, the other contracts from the outside. The two contours are inter-linked to provide a balanced technique with an ability to reject weak’local energy minima. Additionally a dual active contour configuration using dynamic programming has been developed to locate a global energy minimum and complements recent approaches via simulated annealing and genetic algorithms. These differ from conventional evolutionary approaches, where energy minimisation may not converge to extract the target shape, in contrast with the guaranteed convergence of a global approach. The new techniques are demonstrated to extract successfully target shapes in synthetic and real images, with superior performance to previous approaches. The new technique employing dynamic programming is deployed to extract the inner face boundary, along with a conventional normal-driven contour to extract the outer face boundary. Application to a database of 75 subjects showed that the outer contour was extracted successfully for 96% of the subjects and the inner contour was successful for 82%. This application highlights the advantages new dual active contour approaches for automatic shape extraction can confer.
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Pinilla, Gomez Luis Gonzalo. "The face is the only location." Thesis, University of Iowa, 2015. https://ir.uiowa.edu/etd/1725.
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My work is a subjective investigation of a constant transit through the real and symbolic frontiers that create memories. By considering the different cultural and social dimensions of my life as a Latino, I am interested in the relationship with a time that is vital, personal but also shared. Through my research, a constant interaction with the materiality of the print process informs the creation of strategies of auto-recognition that are both personal and collective and introduces me to the acceptance of the affective world, thus of my body and my own temporality. The print represents fragmentary traces of an incomplete cartography, that is at the same time intermittent and selective. They are subjective approaches of a personal narrative that is at the same time collective. This visual narrative reflects on my forced displacement and cultural periphery conscience and the unconscious desire for an unmapped world.
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Bennetts, Jennifer. "The identification and characterisation of novel genes in development /." [St. Lucia, Qld.], 2006. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19375.pdf.
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Khaki, Kazimali M. "Weightless neural networks for face recognition." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/8025.
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The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition. Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition.
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Baldasso, Rosane Pérez. "Emprego da fotoantropometria para compreensão do perfil de envelhecimento facial." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/23/23153/tde-06032017-134731/.
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Muitas são as tentativas de projetar a aparência de uma pessoa através do tempo. Um recurso muito utilizado para este fim é a simulação de progressão de idade, que consiste na modificação de uma fotografia de uma pessoa, representando o efeito do envelhecimento sobre a sua aparência por meio de processamento de imagem digital ou de desenhos artísticos. Porém, as metodologias utilizadas usualmente são subjetivas. A inexistência de uma metodologia cientificamente consolidada capaz de simular mudanças ocorridas na face, por meio de imagens, evidencia a necessidade de estudos na área, visando subsidiar o perito criminal para a execução da técnica e sua aplicação como auxiliar nos exames de Identificação Facial Forense (IFF). A principal premissa envolvida em qualquer método de Identificação Humana é a da \"unicidade\" da informação utilizada como meio de comparação. Por unicidade, entende-se a individualidade (ou não repetitividade) do parâmetro estudado, que deve ser mensurada com a realização de estudos populacionais específicos, sendo que algumas características faciais se mantêm ao longo dos anos. O presente trabalho teve como objetivo avaliar modificações no padrão métrico da face do indivíduo adulto em função da idade, em específico no que diz respeito ao crescimento das orelhas, crescimento de nariz e alteração da espessura dos lábios, visando a compreensão do processo de envelhecimento e o desenvolvimento de metodologias para estimativas de progressão de idade em casos de desaparecidos e foragidos, dando subsídios ao perito criminal para execução da técnica de simulação de envelhecimento da face em imagens. Para tanto, foram utilizadas imagens faciais padronizadas de 700 indivíduos adultos brasileiros, do sexo feminino e do sexo masculino, da região Sul, ancestralidade branca e em idades de 20, 30, 40, 50, 60, 70 e 80 anos, todas em norma frontal oriundas do Sistema Nacional de Passaporte (SINPA), gerenciado pela Polícia Federal. Os resultados apontaram um aumento gradual e mensurável da altura das orelhas, intimamente relacionado com o aumento dos lóbulos, aumento da largura do nariz, bem como a diminuição da porção mucosa dos lábios, levemente mais evidente no lábio inferior, sendo estas alterações progressivas ao longo dos grupos etários estudados e diferente entre os sexos. Assim, concluiu-se ser possível determinar metricamente as modificações que ocorrem na face do indivíduo adulto em função da idade, em específico no que diz respeito a alterações de nariz, lábios e orelhas, visando a compreensão do processo de envelhecimento e fornecendo subsídios para execução da técnica de simulação de progressão de idade da face em imagens.There are many attempts to design the look of a person over time. A widely used resource for this purpose is the simulation of age progression, which is the modification of a person\'s photograph, representing the effect of aging on their appearance through digital image processing or artistic designs. However, the methodologies commonly used are subjective. The absence of a scientifically consolidated methodology capable of simulating changes in the face, through images, highlights the need for studies in the area, in order to support the criminal expert for technical execution, for their application to assist in the Forensic Facial Identification (FFI). The main premise involved in any method of human identification is the \"uniqueness\" of information used as a comparison. For uniqueness means individuality (or non-repeatability) of the studied parameter, which should be measured to perform specific population studies, with some facial features that are maintained over the years. This study evaluated changes in the metric pattern of the face of adult people according to age, in particular with regard to the growth of ears, nose and change in the thickness of the lips, aiming to understand the aging process and the development of methodologies for age progression to estimate in cases of missing persons and fugitives, giving subsidies to perform the face of the aging simulation technique images. Therefore, were used facial images of 700 South Brazilian adults of both sexes, white ancestry and 20 30, 40, 50, 60, 70 and 80 ages, all in the frontal picture, coming from the National Passport System managed by the Federal Police. Results showed a gradual and measurable increase the height of the ears closely related with the increase of the lobes, increased nose width, as well as decreasing mucous portion of the lips slightly more evident in the lower lip, and these incremental changes over the age groups studied and are different between the sexes. Thus, it was found possible to determine metrically changes that occur in the face of adult individuals depending on age, in particular as regards the nose changes, lips and ears, aiming to understand the aging process and providing data to running the technique of simulating the progression of age in face images.
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Rondahl, Thomas. "Face Detection in Digital Imagery Using Computer Vision and Image Processing." Thesis, Umeå universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51406.
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By adding a failure fault limit to an existing implementation of a face detection system application and a tolerance limit for detection time, a desired throughput for detected objects could be established. The aim of this thesis was to add an increased detection rate for pro le/partial faces while increasing the stability and run-time of the system. The results were obtained through an empirical analysis of test data which was compared between the implementation done for this thesis and the older implementation. The results showed an increase in detected faces (in low sized images) by 10% while also increasing the number of false-positives by 0.725 detections per average image. In large size image cases, an automatic scaling functionality was added, to decrease detection time and decrease false-negatives. The results indicated a decrease in average detection time from (old implementation) 15 seconds to 2 seconds, while still increasing positive detection with 23%, from an average of 42% to 65%. False-positives were also decreased from 5.8 to 0.2 detections per average image used in test.
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Qahwaji, Rami S. R. "Detecting edges in noisy face database images." 2003. http://hdl.handle.net/10454/4069.
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Ho, Tzung-Ting, and 何宗庭. "3D face reconstruction with one face image." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/45405982633645119692.
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碩士國防大學中正理工學院
資訊科學研究所
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Reconstructing a realistic 3D face using 2D facial images is still a challenging mission for current techniques. Accurate face modeling has extensive applications in areas such as animation cinema, image-based rendering, and 3D game design. A realistic 3D face can be reconstructed using two or more 2D facial images with manual selected feature points. However, the more 2D facial images are used, the higher complexity is needed. In this thesis, a single 2D frontal facial image-based 3D face reconstructive scheme is proposed. To select the appropriate 3D model, we compare the distances and angles of the selected feature points in 2D image and 3D model. First, the misjudgment for the same face with different scales can be overcome via coordinate transformation. Then, rough 3D models are selected using the distance of feature points. Finally, the angles of the connected feature points are used to decide the most appropriate 3D model. The experimental results demonstrate that the proposed method can decrease the time of reconstructing 3D face, and a realistic 3D face model can be created.