Face Detection has been applied in many fields such as surveillance, human machine interaction, entertainment and health care. Two main reasons for extensive attention on this typical research domain are: 1) a strong need for the face recognition system is obvious due to the widespread use of security, 2) face recognition is more user friendly and faster since it almost requests the users to do nothing. The system is based on ARM Cortex-A8 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature and Viola-Jones algorithm. In the paper, the face Detection system uses the AdaBoost algorithm to detect human face from the frame captured by the camera. The paper introduces the pros and cons between several popular images processing algorithm. Facial expression recognition system involves face detection and emotion feature interpretation, which consists of offline training and online test part. Active shape model (ASM) for facial feature node detection, optical flow for face tracking, support vector machine (SVM) for classification is applied in this research.

<p>Face detection is a preprocessing step for face recognition algorithms. It is the localization of face/faces in an image or image sequence. Once the face(s) are localized, other computer vision algorithms such as face recognition, image compression, camera auto focusing etc are </p><p>applied. Because of the multiple usage areas, there are many research efforts in face processing. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. To build fully </p><p>automated systems, robust and efficient face detection algorithms are required. </p><p> </p><p>Numerous techniques have been developed to detect faces in a single image; in this project we have used a classification-based face detection method using Gabor filter features. We have designed five frequencies corresponding to eight orientations channels for extracting facial features from local images. The feature vector based on Gabor filter is used as the input of the face/non-face classifier, which is a Support Vector Machine (SVM) on a reduced feature </p><p>subspace extracted by using principal component analysis (PCA). </p><p> </p><p>Experimental results show promising performance especially on single face images where 78% accuracy is achieved with 0 false acceptances.</p>

Face detection is a preprocessing step for face recognition algorithms. It is the localization of face/faces in an image or image sequence. Once the face(s) are localized, other computer vision algorithms such as face recognition, image compression, camera auto focusing etc are applied. Because of the multiple usage areas, there are many research efforts in face processing. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. To build fully automated systems, robust and efficient face detection algorithms are required. Numerous techniques have been developed to detect faces in a single image; in this project we have used a classification-based face detection method using Gabor filter features. We have designed five frequencies corresponding to eight orientations channels for extracting facial features from local images. The feature vector based on Gabor filter is used as the input of the face/non-face classifier, which is a Support Vector Machine (SVM) on a reduced feature subspace extracted by using principal component analysis (PCA). Experimental results show promising performance especially on single face images where 78% accuracy is achieved with 0 false acceptances.

In this research we develop a Real-time face tracking system by single pan-tilt camera. The system includes face detection, deformable template tracking and motion control. We refer a method to search the facial features by using the genetic algorithm searching technique, the learning algorithm for face detector is based on AdaBoost. In the face tracking, we refer a tracking way to combine with detection and tracking. In the pan-tilt camera control part, two fuzzy logic controllers are designed to control the tracking and handling of moving face. We achieve a more robust tracking way than the single-template by renewing face-template continuously. Finally in our tests, the system can track the face of people in 30-frame per second under complex environment by using the personal computer.

The aim of the work. The aims of this work are: a) to develop new or modify existing face detection, analysis and recognition methods, in order to increase their speed and accuracy; b) investigate the problems of face features and face contour detection.

Made available in DSpace on 2014-06-12T15:56:57Z (GMT). No. of bitstreams: 2 arquivo3096_1.pdf: 4031500 bytes, checksum: 3cfbafa985058f2171a93b3e230c2c35 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Desde equipamentos de vigillância por vídeo a câmeras digitais e telefones celulares, a detecção de rostos e uma funcionalidade que esta rapidamente ganhando peso no projeto de interfaces de usuario mais inteligentes e tornando a interação homem-maquina cada vez mais natural e intuitiva. Com isto em mente, fabricantes de chips estão embarcando esta tecnologia na sua nova geração de processadores de sinal de imagem (ISP) desenvolvidos especificamente para uso em aparelhos celulares. O foco deste trabalho foi analisar um algoritmo para detecção de rostos para suportar a definição da arquitetura mais adequada a ser usada na solução final. Um algoritmo inicial baseado na tecnica de Cascata de Caracteristicas Simples foi usado como base para este trabalho. O algoritmo inicial, como especificado, leva quase quarenta segundos para processar um unico quadro de imagem no processador alvo, tempo este que inviabilizaria o uso desta solução. Focando na implementação de um novo ISP, o algoritmo foi completamente reescrito, otimizado e propriamente mapeado na plataforma alvo, ao ponto onde um fator de aceleração de 167x foi atingido e uma imagem de pior caso agora leva menos de 250 milissegundos para ser processada. Este numero e ainda mais baixo se for considerada a media em um conjunto maior de imagens ou um vídeo, caindo para cerca de 100 milissegundos por quadro de imagem processado. Não obstante, performance não foi o unico alvo, tambem a quantidade de memoria necessaria foi dramaticamente reduzida. Isto tem um impacto direto na area de silicio requerida pelo circuito e conseq uentemente menores custos de producao e consumo de potência, fatores criticos em um sistema para aplicações moveis. E importante ressaltar que a qualidade não foi deixada de lado e em todas as otimizações realizadas, tomou-se o cuidado de verificar que a qualidade de detecção não tinha sido impactada. Este documento apresenta a pesquisa feita e os resultados obtidos. Começa por uma breve introdução ao assunto de Visão Computacional e aos desafios de projetar uma solução de detecção de rostos. Apos esta introdução, o algoritmo que serviu como base para este trabalho e apresentado juntamente com as otimizações mais relevantes ao nivel algoritmico para melhorar a performance. Na sequência, instruções customizadas desenvolvidas para acelerar a execução do algoritmo na solução final são apresentadas e discutidas

The problem of face detection and recognition within a given database has become one of the important problems in computer vision. A simple approach for Face Detection in video is to run a learning based face detector every frame. But such an approach is computationally expensive and completely ignores the temporal continuity present in videos. Moreover the search space can be reduced by utilizing visual cues extracted based on the relevant task at hand(top down approach). Once detection is done next step is to perform a face recognition based on the available database. But the faces detected from face detect or output is neither aligned nor well cropped and is prone to scale change. We call such faces as free form faces. But the current existing algorithms on face recognition assume faces to be properly aligned and cropped, and having the same scale as the faces in the database, which is highly constrained. In this thesis, we propose an integrated detect-track framework for Multiview face detection in videos. We overcome the limitations of the frame based approaches, by utilizing the temporal continuity present in videos and also incorporating the top down information of the task. We model the problem based on the concept from Experiential sampling [2]. This consists of determining certain key positions which are relevant to the task(face detection). These key positions are referred to as attention samples and Multiview face detection is performed only at these locations. These statistical samples are estimated based on the visual cues, past experience and the temporal continuity and is modeled as a Bayesian filtering problem, which is solved using Particle Filters. In order to detect all views we use a tracker integrated with the detector and come out with a novel track termination algorithm using the concepts from Track Before Detect(TBD)[26]. Such an approach is computationally efficient and also results in lower false positive rate. We provide experiments showing the efficiency of the integrated detect-track approach over the multiview face detector approach without a tracker. For free form face recognition we propose to use the concept of Principal Geodesic Analysis(PGA) of the Covariance descriptors obtained from Gabor filters. This is similar to Principal Component Analysis in Euclidean spaces (Covariance descriptors lie on a Riemannian manifold). Such a descriptor is robust to alignment and scaling problems and also are of lower dimensions. We also employ sparse modeling technique for Face recognition task using these Covariance descriptor which are dimensionally reduced by transforming them on to a tangent space, which we call PGA feature. Further, we improve upon the recognition results of linear sparse modeling, by non-linear mapping of the PGA features by employing “Kernel Trick” for these sparse models. We show that the Kernelized sparse models using the PGA features are indeed very efficient for free form face recognition by testing on two standard databases namely AR and YaleB database.

Human face detection and recognition techniques play an important role in applica- tions like face recognition, video surveillance, human computer interface and face image databases. Using color information in images is one of the various possible techniques used for face detection. The novel technique used in this project was the combination of various techniques such as skin color detection, template matching, gradient face de- tection to achieve high accuracy of face detection in frontal faces. The objective in this work was to determine the best rotation angle to achieve optimal detection. Also eye and mouse template matching have been put to test for feature detection.

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<p>Face detection is a classical application of object detection. There are many practical applications in which face detection is the first step; face recognition, video surveillance, image database management, video coding. </p><p>This report presents the results of an implementation of the AdaBoost algorithm to train a Strong Classifier to be used for face detection. The AdaBoost algorithm is fast and shows a low false detection rate, two characteristics which are important for face detection algorithms. </p><p>The application is an implementation of the AdaBoost algorithm with several command-line executables that support testing of the algorithm. The training and detection algorithms are separated from the rest of the application by a well defined interface to allow reuse as a software library. </p><p>The source code is documented using the JavaDoc-standard, and CppDoc is then used to produce detailed information on classes and relationships in html format. </p><p>The implemented algorithm is found to produce relatively high detection rate and low false alarm rate, considering the badly suited training data used.</p>