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1
Cöster, Jonatan, and Michael Ohlsson. "Human Attention : The possibility of measuring human attention using OpenCV and the Viola-Jones face detection algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166584.
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The question of whether an audience is focused and attentive can be of great importance. Research shows that a main concern during lectures is the varying level of attention from the students. Getting real time feedback on the students attention could give the lecturer an insight into what can be improved in terms of the material being presented. One potential way to get this feedback is to have a face detection algorithm to measure when someone is paying attention. The objective of the study is to investigate if it is possible to measure a person’s attention in a controlled environment using the OpenCV programming library and the Viola-Jones algorithm. In order to measure if someone was paying attention, a definition of attention was required. It is obvious to humans when someone is paying attention. However, this is not the case for a computer. A data set consisting of pictures of attentive and inattentive subjects was used to evaluate whether the software could be used to measure attention. The results of the study showed that OpenCV had an almost perfect detection rate with few false positives. The conclusion is therefore that the OpenCV programming library could be used to measure attention in a controlled environment. However, due to the limited scope of the study, further investigations are required in order to use it in a real-world application.
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Марченко, Ігор Олександрович, Игорь Александрович Марченко, Ihor Oleksandrovych Marchenko, Сергій Олександрович Петров, Сергей Александрович Петров, and Serhii Oleksandrovych Petrov. "Модифікація алгоритму Віоли-Джонса шляхом аналізу регіонів з визначеною текстурою." Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47008.
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Відомим недоліком алгоритму Віоли-Джонса є послідовна обробка всього зображення, незважаючи на особливості об‘єкту, що локалізується або розпізнається [1]. Це особливо уповільнює алгоритм у випадках складного фону або малого розміру об‘єкту відносно всього зображення.
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RODRIGUES, Matheus Bezerra Estrela. "Estudo da aplicação do algoritmo Viola-Jones à detecção de pneus com vistas ao reconhecimento de automóveis." Universidade Federal de Campina Grande, 2012. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1861.
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MATHEUS BEZERRA ESTRELA RODRIGUES - DISSERTAÇÃO PPGCC 2012..pdf: 7068761 bytes, checksum: 4b1283a1da5ca466fcf0357c33091a30 (MD5)Made available in DSpace on 2018-10-01T15:06:04Z (GMT). No. of bitstreams: 1 MATHEUS BEZERRA ESTRELA RODRIGUES - DISSERTAÇÃO PPGCC 2012..pdf: 7068761 bytes, checksum: 4b1283a1da5ca466fcf0357c33091a30 (MD5) Previous issue date: 2012-02-29
Impulsionado pelo crescimento no uso de vigilância eletrônica, essa pesquisa introduz o uso de uma técnica que demonstra eficiência no reconhecimento de faces em imagens, alterando o objeto de busca para pneus de veículos, visando o reconhecimento da presença do veículo na cena. A técnica aplicada para o reconhecimento é o algoritmo Viola-Jones. Essa técnica é dividida em dois momentos: o treinamento e a detecção. Na primeira etapa, vários treinamentos são executados, usando aproximadamente 7000 imagens diferentes. Para a etapa final, um detector de faces foi adaptado para reconhecer pneus, utilizando o treinamento da etapa anterior, e sua eficiência em reconhecer os pneus foi comparável à eficiência do detector de faces que usa treinamento de referência da biblioteca em software que é referência nesta área, OpenCV. O detector desenvolvido apresentou taxa de reconhecimento de 77%, quando o reconhecimento de faces obteve 80%. A taxa de falsos negativos também foi próxima, apresentando o detector de pneus 2% e o de faces 1%.
Motivated by the growing use of electronic surveillance, this research introduces the use of the Viola-Jones algorithm, which is known to be efficient in recognition of human faces in images, changing the object to be recognized to vehicle tires, aiming to detect vehicles in a scene. This approach divides the process in two steps: training and detection. Training was done using around 7000 different images of vehicles. For the detection step, work was done to adapt a face detector to detect vehicles tires. The tire detector was compared to a face detector that used a reference training for faces from OpenCV library. The tire detector showed 77% efficiency, whereas the face detector showed 80%. False negative numbers also showed similar closeness, as 2% for the tire detector and 1% for the reference face detector.
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Bernátek, Pavel. "Vizualizace pulzu ve videozáznamu obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242090.
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In the semestral thesis is given basic methods of non-contact measurement heart rate. There is explained Eulerian video magnification method deals with the visualization of the pulse in the videosequence of face. The semestral thesis describes algorithm Viola-Jones face detection in images and algorithm Kanade-Lucas-Tomasi for tracking faces in the videosequence. Part of the work includes design and realization of measurement. There is explained realization of the program and documented execution results, which are discussed. From the results it is designed to guide for optimal recording.
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Krolikowski, Martin. "Automatické detekce obličeje a jeho jednotlivých částí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217286.
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The master thesis presents an overview of face detection task in color, static images. Face detection term is posed in the context of various branches. Main concepts of face detection and also their relationships are described. Individual approaches are divided into groups and then define in turn. In the thesis is in detail described algorithm AdaBoost, which is selected on the basis of its properties. Especially speed of computation and good detection results are key features. In the scope of this work Viola-Jones detector was implemented. This detector was trained with face pictures from public accessible database. Combination of Viola-Jones detector with simple color detector is described. In the thesis is also presented experiment approach to facial features detection.
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Pathare, Sneha P. "Detection of black-backed jackal in still images." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97023.
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Thesis (MSc)--Stellenbosch University, 2015.ENGLISH ABSTRACT: In South Africa, black-back jackal (BBJ) predation of sheep causes heavy losses to sheep farmers. Different control measures such as shooting, gin-traps and poisoning have been used to control the jackal population; however, these techniques also kill many harmless animals, as they fail to differentiate between BBJ and harmless animals. In this project, a system is implemented to detect black-backed jackal faces in images. The system was implemented using the Viola-Jones object detection algorithm. This algorithm was originally developed to detect human faces, but can also be used to detect a variety of other objects. The three important key features of the Viola-Jones algorithm are the representation of an image as a so-called ”integral image”, the use of the Adaboost boosting algorithm for feature selection, and the use of a cascade of classifiers to reduce false alarms. In this project, Python code has been developed to extract the Haar-features from BBJ images by acting as a classifier to distinguish between a BBJ and the background. Furthermore, the feature selection is done using the Asymboost instead of the Adaboost algorithm so as to achieve a high detection rate and low false positive rate. A cascade of strong classifiers is trained using a cascade learning algorithm. The inclusion of a special fifth feature Haar feature, adapted to the relative spacing of the jackal’s eyes, improves accuracy further. The final system detects 78% of the jackal faces, while only 0.006% of other image frames are wrongly identified as faces.
AFRIKAANSE OPSOMMING: Swartrugjakkalse veroorsaak swaar vee-verliese in Suid Afrika. Teenmaatreels soos jag, slagysters en vergiftiging word algemeen gebruik, maar is nie selektief genoeg nie en dood dus ook vele nie-teiken spesies. In hierdie projek is ’n stelsel ontwikkel om swartrugjakkals gesigte te vind op statiese beelde. Die Viola-Jones deteksie algoritme, aanvanklik ontwikkel vir die deteksie van mens-gesigte, is hiervoor gebruik. Drie sleutel-aspekte van hierdie algoritme is die voorstelling van ’n beeld deur middel van ’n sogenaamde integraalbeeld, die gebruik van die ”Adaboost” algoritme om gepaste kenmerke te selekteer, en die gebruik van ’n kaskade van klassifiseerders om vals-alarm tempos te verlaag. In hierdie projek is Python kode ontwikkel om die nuttigste ”Haar”-kenmerke vir die deteksie van dié jakkalse te onttrek. Eksperimente is gedoen om die nuttigheid van die ”Asymboost” algoritme met die van die ”Adaboost” algoritme te kontrasteer. ’n Kaskade van klassifiseerders is vir beide van hierdie tegnieke afgerig en vergelyk. Die resultate toon dat die kenmerke wat die ”Asymboost” algoritme oplewer, tot laer vals-alarm tempos lei. Die byvoeging van ’n spesiale vyfde tipe Haar-kenmerk, wat aangepas is by die relatiewe spasieëring van die jakkals se oë, verhoog die akkuraatheid verder. Die uiteindelike stelsel vind 78% van die gesigte terwyl slegs 0.006% ander beeld-raampies verkeerdelik as gesigte geklassifiseer word.
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Brunclík, Robert. "Automatická regulace velikosti písma podle vzdálenosti čtenáře." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241995.
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The thesis deals with automatic control the font size by the distance from the reader. It includes theoretical acquaintance with the face detection and subsequent tracking of the detected area during the scene. Furthermore, there is a comparison of the tracking algorithms. Then the calculation of distance is decribed. It is based on the user’s calibration and based on the outcome occurs the font size is automatically corrected. There is also a description of a separate application Automatical controller of the text size, with the recommended settings of the program.
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Тарановський, Антон Володимирович, Антон Владимирович Тарановский, Anton Volodymyrovych Taranovskyi, Сергій Олександрович Петров, Сергей Александрович Петров, and Serhii Oleksandrovych Petrov. "Визначення оптимальних параметрів вхідного зображення на характеристики розпізнавання з використанням алгоритму Віола-Джонса." Thesis, Видавництво СумДУ, 2013. http://essuir.sumdu.edu.ua/handle/123456789/42602.
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Об’єктом аналізу алгоритмів, що реалізуються в
системах комп’ютерного зору та працюють у рамках
вирішення питання детекції та розпізнавання образів, є
відео- та фотоматеріали, що є результатом спостереження
фото- та відеокамер. Характеристики вхідних даних
різняться в залежності від технічних можливостей камер.
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Марченко, Ігор Олександрович, Сергій Олександрович Петров, Сергей Александрович Петров, Serhii Oleksandrovych Petrov, Игорь Александрович Марченко, and Ihor Oleksandrovych Marchenko. "Підвищення якості розпізнавання алгоритму Віоли-Джонса шляхом попередньої обробки зображень." Thesis, Видавництво ПНПУ ім. К.Д. Ушинського, 2015. http://essuir.sumdu.edu.ua/handle/123456789/42803.
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Розглянуто можливі шляхи оптимізації алгоритму Віоли-Джонса без модифікації базового підходу. З метою підвищення результатів розпізнавання запропоновано проводити попередню обробку вхідного зображення з використанням эфектів, таких як яскравість, контраст. В результаті якість розпізнавання підвищилась на 37.39%.Рассмотрены возможные оптимизации алгоритма Виолы-Джонса без модификации базового алгоритма. С целью повышения качества распознавания предложено выполнять предварительную обработку изображения с помощью фильтров, таких как, яркость, контраст. В результате качество распознавания повысилось на 37,39%.
The paper showed improving of the detection rate without modifying the base algorithm. Detection rate improved by decreasing defects on the image. Changing brightness and contrast is one way to modify an image. The detection rate increased by 37.39%.
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Chmelíková, Lucie. "Bezkontaktní měření tepové frekvence z obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241972.
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This thesis deals with the study of contactless and noninvasive methods for estimation of heart rate. Contactless measurement is based on capturing person faces by video camera and from sequences of pictures are estimated values of the heart rate. The theoretical part describes heart rate and methods that are being used to estimate heart rate from color changes in the face. It also contains testing of tracking algorithms. Practical part deals with user interface of program for contactless measurement of heart rate and its software solution. Thesis also contains statistical evaluation of program functionality.
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Husain, Benafsh Nadir. "Face Detection And Lip Localization." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/601.
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Integration of audio and video signals for automatic speech recognition has become an important field of study. The Audio-Visual Speech Recognition (AVSR) system is known to have accuracy higher than audio-only or visual-only system. The research focused on the visual front end and has been centered around lip segmentation. Experiments performed for lip feature extraction were mainly done in constrained environment with controlled background noise. In this thesis we focus our attention to a database collected in the environment of a moving car which hampered the quality of the imagery.
We first introduce the concept of illumination compensation, where we try to reduce the dependency of light from over- or under-exposed images. As a precursor to lip segmentation, we focus on a robust face detection technique which reaches an accuracy of 95%. We have detailed and compared three different face detection techniques and found a successful way of concatenating them in order to increase the overall accuracy. One of the detection techniques used was the object detection algorithm proposed by Viola-Jones. We have experimented with different color spaces using the Viola-Jones algorithm and have reached interesting conclusions.
Following face detection we implement a lip localization algorithm based on the vertical gradients of hybrid equations of color. Despite the challenging background and image quality, success rate of 88% was achieved for lip segmentation.
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Korchakov, Sergei. "Zpracování obrazu v systému Android - detekce a rozpoznání obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220900.
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This master’s Thesis focuses on image processing on Android platform and development of an application, that is able to do face detection and recognition in real scene. Thesis gives highlight of modern algorithms of face detection. It first examines and compares the standard features of Android platform (FaceDetector a FaceDetectionListener) and JJIL, OpenIMAJ, OpenCV libraries experiment, and presents the results. For purposes of face recognition was selected OpenCV library. Three different algorithms of identification were tested: FisherFaces, EigenFaces a Local Binary Patterns Histograms. Based on performance comparison best methods were implemented in developed application.
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Assaad, Firas Souhail. "Biometric Multi-modal User Authentication System based on Ensemble Classifier." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418074931.
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Benda, Ondřej. "Návrh rozhodovacích stromů na základě evolučních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219457.
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Tato diplomová práce pojednává o dvou algoritmech pro dolování z proudu dat - Very Fast Decision Tree (VFDT) a Concept-adapting Very Fast Decision Tree (CVFDT). Je vysvětlen princip klasifikace rozhodovacím stromem. Je popsána základní myšlenka konstrukce stromu Hoeffding Tree, který je základem pro algoritmy VFDT a CVFDT. Tyto algoritmy jsou poté rozebrány detailněji. Dále se tato práce zabývá návrhem algoritmu Genetického Programování (GP), který je použit pro vytváření klasifikátoru obrazových dat. Vytvořený klasifikátor je použit jako alternativní způsob klasifikace objektů v obraze ve frameworku Viola-Jones. V práci je rozebrána implementace algoritmů, které jsou implementovány v jazyce Java. Algoritmus GP je integrován do knihovny “Image Processing Extension” programu RapidMiner. Algoritmy VFDT a CVFDT jsou testovány na syntetických a reálných textových datech. Algoritmus GP je testován na klasifikaci obrazových dat a následně vytvořený klasifikátor je otestován na detekci obličejů v obraze.
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Al-Dahoud, A., and Hassan Ugail. "A method for location based search for enhancing facial feature design." 2016. http://hdl.handle.net/10454/9482.
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NoIn this paper we present a new method for accurate real-time facial feature detection. Our method is based on local feature detection and enhancement. Previous work in this area, such as that of Viola and Jones, require looking at the face as a whole. Consequently, such approaches have increased chances of reporting negative hits. Furthermore, such algorithms require greater processing power and hence they are especially not attractive for real-time applications. Through our recent work, we have devised a method to identify the face from real-time images and divide it into regions of interest (ROI). Firstly, based on a face detection algorithm, we identify the face and divide it into four main regions. Then, we undertake a local search within those ROI, looking for specific facial features. This enables us to locate the desired facial features more efficiently and accurately. We have tested our approach using the Cohn-Kanade’s Extended Facial Expression (CK+) database. The results show that applying the ROI has a relatively low false positive rate as well as provides a marked gain in the overall computational efficiency. In particular, we show that our method has a 4-fold increase in accuracy when compared to existing algorithms for facial feature detection.
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Candra, Henry. "Emotion recognition using facial expression and electroencephalography features with support vector machine classifier." Thesis, 2017. http://hdl.handle.net/10453/116427.
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University of Technology Sydney. Faculty of Engineering and Information Technology.Recognizing emotions from facial expression and electroencephalography (EEG) emotion signals are complicated tasks that require substantial issues to be solved in order to achieve higher performance of the classifications, i.e. facial expression has to deal with features, features dimensionality, and classification processing time, while EEG emotion recognition has the concerned with features, number of channels and sub band frequency, and also non-stationary behaviour of EEG signals. This thesis addresses the aforementioned challenges. First, a feature for facial expression recognition using a combination of Viola-Jones algorithm and improved Histogram of Oriented Gradients (HOG) descriptor termed Edge-HOG or E–HOG is proposed which has the advantage of insensitivity to lighting conditions. The issue of dimensionality and classification processing time was resolved using a combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which has successfully reduced both the dimension and the classification processing time resulting in a new low dimension of feature called Reduced E–HOG (RED E–HOG). In the case of EEG emotion recognition, a method to recognize 4 discrete emotions from arousal-valence dimensional plane using wavelet energy and entropy features was developed. The effects of EEG channel and subband selection were also addressed, which managed to reduce the channels from 32 to 18 channels and the subband from 5 to 3 bands. To deal with the non-stationary behaviour of EEG signals, an Optimal Window Selection (OWS) method as feature-agnostic pre-processing was proposed. The main objective of OWS is window segmentation with varying window which was applied to 7 various features to improve the classification results of 4 dimensional plane emotions, namely arousal, valence, dominance, and liking, to distinguish between the high or low state of the aforementioned emotions. The improvement of accuracy makes the OWS method a potential solution to dealing with the non-stationary behaviour of EEG signals in emotion recognition. The implementation of OWS provides the information that the EEG emotions may be appropriately localized at 4–12 seconds time segments. In addition, a feature concatenating of both Wavelet Entropy and average Wavelet Approximation Coefficients was developed for EEG emotion recognition. The SVM classifier trained using this feature provides a higher classification result consistently compared to various different features such as: simple average, Fast Fourier Transform (FFT), and Wavelet Energy. In all the experiments, the classification was conducted using optimized SVM with a Radial Basis Function (RBF) kernel. The RBF kernel parameters were properly optimized using a particle swarm ensemble clustering algorithm called Ensemble Rapid Centroid Estimation (ERCE). The algorithm estimates the number of clusters directly from the data using swarm intelligence and ensemble aggregation. The SVM is then trained using the optimized RBF kernel parameters and Sequential Minimal Optimization (SMO) algorithm.
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Ferreira, Sónia Maria Gomes de Amaral. "Assisted analysis of ocular movements in the parameterization of cochlear implants." Master's thesis, 2011. http://hdl.handle.net/10316/17608.
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Os implantes cocleares são muito usados em crianças em casos de surdez
neurossensorial, para restaurar a capacidade auditiva e aumentar o desempenho
fonético e verbal. O ganho de cada eléctrodo do implante deve ser adaptado por
uma equipa de reabilitação, após a sua implantação. As técnicas de análise
assistida de movimentos oculares podem optimizar este balanceamento de
eléctrodos, através do estudo da relação entre os movimentos oculares e os
estímulos sonoros.
O sistema remoto de seguimento ocular em desenvolvimento neste trabalho é
constituído por três câmaras e segue duas linhas principais: detecção e seguimento
da face e dos olhos. Foram desenvolvidos em MALTALB® e SIMULINK® três
algoritmos para a detecção e seguimento da face (Red-Green, Mahalanobis
Distance e Viola-Jones) e dois algoritmos para detectar a região dos olhos
(Between The Eyes e Eye Gabor Filter). Os algoritmos foram testados em 6
vídeos de 23 voluntários, contendo cada vídeo um tipo diferente de movimento de
cabeça.
O Viola-Jones foi considerado o melhor método para a face com uma taxa de
detecção de 73.91%. O Eye Gabor Filter foi considerado o melhor método para
detectar a região dos olhos com uma taxa de detecção de 76.81%, quando
implementado com o Viola-Jones. A velocidade de processamento do Viola-Jones
associado ao Eye Gabor Filter foi de 18 frames/segundo.
A robustez relativa aos movimentos de cabeça e a velocidade de processamento
dos algoritmos anteriores devem ser melhoradas no futuro.
Palavras-Chave:
Implante Coclear; Balanceamento de Eléctrodos; Detecção do Olho; Detecção da
Face; Seguimento do Olho; Seguimento da Face; Ponto de Fixação.