Relevant bibliographies by topics / Face Image

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Face Image'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Face Image"

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Awhad, Rahul, Saurabh Jayswal, Adesh More, and Jyoti Kundale. "Fraudulent Face Image Detection." ITM Web of Conferences 32 (2020): 03005. http://dx.doi.org/10.1051/itmconf/20203203005.

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Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.
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Hu, Chang Jie, and Hong Li Xu. "Face Image Segmentation Technology Research." Advanced Materials Research 846-847 (November 2013): 1339–42. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1339.

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Face contains the very rich information, which is a typical biological feature .It has a wide application prospect in personal identification, intelligent video surveillance and human-computer interaction. Face detection is to determine the number, the location, size and other information of all the faces among the color images that have been input. Firstly, skin color model is established, and then we use the skin color model to convert color image to gray image, and then we can denoise gray image, at last use the Fisher criterion to obtain the dynamic threshold segmentation of the face image, so as to lay a good foundation for the location of the face region. Through the experiment we can see, the selection of dynamic threshold, for different detecting images, obtained better color segmentation.
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Du, Cheng, and Biao Leng. "Tunnel Face Image Segmentation Optimization." Applied Mechanics and Materials 397-400 (September 2013): 2148–51. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2148.

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With the development of Transportation Highway and railroad build, mining tunnel geological exploration in the road construction in the proportion of great. This paper presents a design of image processing software of Geological Engineering images for automatic analysis and processing. At present, the technology of image processing, most algorithms are based on the specific image information of specific analysis, and the face image is very complicated, different regions, and even the same construction sections in different areas of the face image may have very big difference. For the tunnel excavation face of digital image processing algorithms have little, need to start from scratch. This paper describes the use of digital image processing technology of Geological Engineering image image segmentation, found on the rock face, through the comparison of edge detection operator and Sobel Gauss - Laplasse operator methods advantages and disadvantages, a value of two images as the processing object image processing algorithm. The technology of Geological Engineering image analysis on tunnel construction period prediction plays a very important role.
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Saha, Rajib, Debotosh Bhattacharjee, and Sayan Barman. "Comparison of Different Face Recognition Method Based On PCA." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 10, no. 4 (November 4, 2014): 2016–22. http://dx.doi.org/10.24297/ijmit.v10i4.626.

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This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Eucleadian distance & Chebychev distance for face matching.
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Xin, Jingwei, Nannan Wang, Xinrui Jiang, Jie Li, Xinbo Gao, and Zhifeng Li. "Facial Attribute Capsules for Noise Face Super Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12476–83. http://dx.doi.org/10.1609/aaai.v34i07.6935.

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Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.
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BEBIS, GEORGE, SATISHKUMAR UTHIRAM, and MICHAEL GEORGIOPOULOS. "FACE DETECTION AND VERIFICATION USING GENETIC SEARCH." International Journal on Artificial Intelligence Tools 09, no. 02 (June 2000): 225–46. http://dx.doi.org/10.1142/s0218213000000161.

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We consider the problem of searching for the face of a particular individual in a two-dimensional intensity image. This problem has many potential applications such as locating a person in a crowd using images obtained by surveillance cameras. There are two steps in solving this problem: first, face regions must be extracted from the image(s) (face detection) and second, candidate faces must be compared against a face of interest (face verification). Without any a-priori knowledge about the location and size of a face in an image, every possible image location and face size should be considered, leading to a very large search space. In this paper, we propose using Genetic Algorithms (GAs) for searching the image efficiently. Specifically, we use GAs to find image sub-windows that contain faces and in particular, the face of interest. Each sub-window is evaluated using a fitness function containing two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been derived using the theory of eigenspaces. A set of increasingly complex scenes demonstrate the performance of the proposed genetic-search approach.
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Chapman, Angus F., Hannah Hawkins-Elder, and Tirta Susilo. "How robust is familiar face recognition? A repeat detection study of more than 1000 faces." Royal Society Open Science 5, no. 5 (May 2018): 170634. http://dx.doi.org/10.1098/rsos.170634.

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Recent theories suggest that familiar faces have a robust representation in memory because they have been encountered over a wide variety of contexts and image changes (e.g. lighting, viewpoint and expression). By contrast, unfamiliar faces are encountered only once, and so they do not benefit from such richness of experience and are represented based on image-specific details. In this registered report, we used a repeat detection task to test whether familiar faces are recognized better than unfamiliar faces across image changes. Participants viewed a stream of more than 1000 celebrity face images for 0.5 s each, any of which might be repeated at a later point and has to be detected. Some participants saw the same image at repeats, while others saw a different image of the same face. A post-experimental familiarity check allowed us to determine which celebrities were and were not familiar to each participant. We had three predictions: (i) detection would be better for familiar than unfamiliar faces, (ii) detection would be better across same rather than different images, and (iii) detection of familiar faces would be comparable across same and different images, but detection of unfamiliar faces would be poorer across different images. We obtained support for the first two predictions but not the last. Instead, we found that repeat detection of faces, regardless of familiarity, was poorer across different images. Our study suggests that the robustness of familiar face recognition may have limits, and that under some conditions, familiar face recognition can be just as influenced by image changes as unfamiliar face recognition.
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Kim, Sanghyuk, Yuseok Ban, Changhyun Park, and Sangyoun Lee. "3D Face Modeling using Face Image." Journal of International Society for Simulation Surgery 2, no. 1 (June 10, 2015): 10–12. http://dx.doi.org/10.18204/jissis.2015.2.1.010.

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Chen, Qi, Li Yang, Dongping Zhang, Ye Shen, and Shuying Huang. "Face Deduplication in Video Surveillance." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 03 (November 22, 2017): 1856001. http://dx.doi.org/10.1142/s0218001418560013.

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The video surveillance system based on face analysis has played an increasingly important role in the security industry. Compared with identification methods of other physical characteristics, face verification method is easy to be accepted by people. In the video surveillance scene, it is common to capture multiple faces belonging to a same person. We cannot get a good result of face recognition if we use all the images without considering image quality. In order to solve this problem, we propose a face deduplication system which is combined with face detection and face quality evaluation to obtain the highest quality face image of a person. The experimental results in this paper also show that our method can effectively detect the faces and select the high-quality face images, so as to improve the accuracy of face recognition.
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Liu, Jing, and Muhammad Aqeel Ashraf. "Face recognition method based on GA-BP neural network algorithm." Open Physics 16, no. 1 (December 31, 2018): 1056–65. http://dx.doi.org/10.1515/phys-2018-0126.

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Abstract In order to recognize faces, face recognition methods need to be studied. When a face is identified by the current method, the image denoising effect is poor, the face image recognition result thus has error, the time used to recognize the face image is long, the signal to noise ratio, the recognition result and the recognition efficiency are low. Based on the GA-BP neural network algorithm, a face recognition method is proposed. A mixed denoising model of face images is constructed by combining dictionary based sparse representation with non-local similarity. The principal component analysis method is used to extract the feature of the face image after denoising and staining the eigenvector of the face image. The GA-BP neural network algorithm is used to optimize the initial weights and thresholds so as to achieve the optimal value. The feature vectors of face images are ted into the genetic neural network to complete face recognition. Experimental results show that the proposed method has high signal-to-noise ratio, accuracy and recognition efficiency.
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Dissertations / Theses on the topic "Face Image"

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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 Telecomunicações
Infrared 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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Books on the topic "Face Image"

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Kemp, Sandra. Future face: Image, identity, innovation. London: Profile Books, 2004.

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Bartlett, Marian Stewart. Face image analysis by unsupervised learning. Boston: Kluwer Academic Publishers, 2001.

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Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001.

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Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1637-8.

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Hitler's face: The biography of an image. Philadelphia: University of Pennsylvania Press, 2006.

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The Image of Edessa. Leiden: Brill, 2009.

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Kawulok, Michal, M. Emre Celebi, and Bogdan Smolka, eds. Advances in Face Detection and Facial Image Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25958-1.

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Le corps désirable: Hommes et femmes face à leur poids. Paris: Presses universitaires de France, 2010.

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Autobiography of a face. New York: Perennial, 2003.

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Malaguarnera, Serafino. L'anorexie face au miroir: Le déclin de la fonction paternelle. Paris: L'Harmattan, 2010.

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Book chapters on the topic "Face Image"

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Cromhout, Gavin, Josh Fallon, Nathan Flood, Katy Freer, Jim Hannah, Adrian Luna, Douglas Mullen, Francine Spiegel, and James Widegren. "Sequences: Morphing an Image." In Photoshop Face to Face, 195–213. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-5137-8_7.

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Ravaut, Frédéric, and Georges Stamon. "Face Image Processing Supporting Epileptic Seizure Analysis." In Face Recognition, 610–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_40.

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Behnke, Sven. "Face Localization." In Hierarchical Neural Networks for Image Interpretation, 191–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45169-3_10.

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Sedmidubsky, Jan, Vladimir Mic, and Pavel Zezula. "Face Image Retrieval Revisited." In Similarity Search and Applications, 204–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25087-8_19.

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Ko, Jaepil, Eunju Kim, and Heyran Byun. "Illumination Normalized Face Image for Face Recognition." In Lecture Notes in Computer Science, 654–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-70659-3_68.

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Vetter, Thomas, and Volker Blanz. "Generalization to Novel Views from a Single Face Image." In Face Recognition, 310–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_16.

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Ylioinas, Juha, Juho Kannala, Abdenour Hadid, and Matti Pietikäinen. "Face Recognition Using Smoothed High-Dimensional Representation." In Image Analysis, 516–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_44.

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De Rosa, Maria Paola, Alessandro Micarelli, and Giuseppe Sansonetti. "An Integrated System for Automatic Face Recognition." In Image Analysis, 140–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_20.

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Wang, Jianguo, and Shucai Fu. "Using Original Face Image and Its Virtual Image for Face Recognition." In Lecture Notes in Computer Science, 231–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67777-4_20.

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Ardabilian, Mohsen, Przemyslaw Szeptycki, Di Huang, and Liming Chen. "3D Face Recognition." In Signal and Image Processing for Biometrics, 89–115. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118561911.ch5.

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Conference papers on the topic "Face Image"

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Saxen, Frerk, Sebastian Handrich, Philipp Werner, Ehsan Othman, and Ayoub Al-Hamadi. "Detecting Arbitrarily Rotated Faces for Face Analysis." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803631.

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Furuie, Ryo, Yuji Goda, and Lifeng Zhang. "Detecting Fake Face Input for Face Authentication by DCT with Compensating the Main Spindle Position of Face." In The 3rd IIAE International Conference on Intelligent Systems and Image Processing 2015. The Institute of Industrial Application Engineers, 2015. http://dx.doi.org/10.12792/icisip2015.032.

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Gupta, Sandesh, Shashank Kapoor, and Phalguni Gupta. "Frontal face generation from profile face image." In 2011 International Conference on Anti-Counterfeiting, Security and Identification (2011 ASID). IEEE, 2011. http://dx.doi.org/10.1109/asid.2011.5967417.

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Kim, Hyung-Il, Seung Ho Lee, and Man Ro Yong. "Face image assessment learned with objective and relative face image qualities for improved face recognition." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351562.

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Xu, Xiang, and Ioannis A. Kakadiaris. "FaRE: Open Source Face Recognition Performance Evaluation Package." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803411.

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Hormann, Stefan, Zhenxiang Cao, Martin Knoche, Fabian Herzog, and Gerhard Rigoll. "Face Aggregation Network For Video Face Recognition." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506037.

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Lin, Jie, Zechao Li, and Jinhui Tang. "Discriminative Deep Hashing for Scalable Face Image Retrieval." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/315.

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With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.
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Ansari, A.-nasser, Mohamed Abdel-Mottaleb, and Mohammad H. Mahoor. "Disparity-Based 3D Face Modeling for 3D Face Recognition." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312416.

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Subasic, M., S. Loncaric, T. Petkovic, H. Bogunovic, and V. Krivec. "Face image validation system." In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. IEEE, 2005. http://dx.doi.org/10.1109/ispa.2005.195379.

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Turkcan, Mehmet Kerem, Ege Cetin, and Tayfun Akgul. "Face-looking Image Recognition." In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE, 2019. http://dx.doi.org/10.1109/siu.2019.8806499.

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Reports on the topic "Face Image"

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Beveridge, J. R., P. J. Phillips, G. H. Givens, B. A. Draper, M. N. Teli, and D. S. Bolme. When high quality face image match poorly. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7759.

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Grother, Patrick J., George W. Quinn, and P. Jonathon Phillips. Report on the evaluation of 2D still-image face recognition algorithms. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7709.

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Heisele, Bernd, Tomaso poggio, and Massimilinao Pontil. Face Detection in Still Gray Images. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada459705.

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Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1812627.

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Quinn, George W., and Patrick J. Grother. Performance of face recognition algorithms on compressed images. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7830.

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Torralba, Antonio, and Pawan Sinha. Detecting Faces in Impoverished Images. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada636815.

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Nguyen, N. C., and J. Peraire. An Interpolation Method for the Reconstruction and Recognition of Face Images. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada471235.

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Son, Jihyeong, NIgel AR Joseph, and Vicki McCracken. Put Faces to Your Instagram Posts. Elements for a Fashion Brand�s Social Media Images to Help Overcome the �Algorithm�. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.10232.

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