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‣ A novel polar-based human face recognition computational model

ZANA, Y.; MENA-CHALCO, J.P.; CESAR JR., R.M.
Fonte: Associação Brasileira de Divulgação Científica Publicador: Associação Brasileira de Divulgação Científica
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
68.13753%
Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns...

‣ Reconhecimento de faces humanas usando redes neurais MLP; Human face recognition using MLP neural networks

Gaspar, Thiago Lombardi
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 15/02/2006 Português
Relevância na Pesquisa
77.961587%
O objetivo deste trabalho foi desenvolver um algoritmo baseado em redes neurais para o reconhecimento facial. O algoritmo contém dois módulos principais, um módulo para a extração de características e um módulo para o reconhecimento facial, sendo aplicado sobre imagens digitais nas quais a face foi previamente detectada. O método utilizado para a extração de características baseia-se na aplicação de assinaturas horizontais e verticais para localizar os componentes faciais (olhos e nariz) e definir a posição desses componentes. Como entrada foram utilizadas imagens faciais de três bancos distintos: PICS, ESSEX e AT&T. Para esse módulo, a média de acerto foi de 86.6%, para os três bancos de dados. No módulo de reconhecimento foi utilizada a arquitetura perceptron multicamadas (MLP), e para o treinamento dessa rede foi utilizado o algoritmo de aprendizagem backpropagation. As características faciais extraídas foram aplicadas nas entradas dessa rede neural, que realizou o reconhecimento da face. A rede conseguiu reconhecer 97% das imagens que foram identificadas como pertencendo ao banco de dados utilizado. Apesar dos resultados satisfatórios obtidos, constatou-se que essa rede não consegue separar adequadamente características faciais com valores muito próximos...

‣ Extração de características de imagens de faces humanas através de wavelets, PCA e IMPCA; Features extraction of human faces images through wavelets, PCA and IMPCA

Bianchi, Marcelo Franceschi de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 10/04/2006 Português
Relevância na Pesquisa
78.0905%
Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente...

‣ Improving face recognition with multispectral fusion and support vector machines

Chiachia, Giovani
Fonte: Universidade Estadual Paulista (UNESP) Publicador: Universidade Estadual Paulista (UNESP)
Tipo: Dissertação de Mestrado Formato: 86 f. : il. color.
Português
Relevância na Pesquisa
68.20258%
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Pós-graduação em Ciência da Computação - IBILCE; O reconhecimento facial é uma das principais formas de identificação humana. Apesar das pesquisas em reconhecimento facial automático terem crescido substancialmente ao longo dos últimos 35 anos, identificar pessoas a partir da face continua sendo um desafio para as áreas de Visão Computacional e Reconhecimento de Padrões. Em função dos cenários variarem desde a identificação a partir de fotografias até o reconhecimento baseado em vídeos sem nenhum tipo de controle ao serem gravados, os maiores desafios estão relacionados à independência contra diferentes tipos de iluminação, pose e expressão. O objetivo desta dissertação é propor técnicas que possam contribuir para a melhoria dos sistemas de reconhecimento facial. A primeira técnica endereça o problema da iluminação através da fusão dos espectros visível e infravermelho da face. Através desta abordagem, as taxas de reconhecimento foram melhoradas em 2.07% enquanto a taxa de erro igual (EER) foi reduzida em 45.47%. A segunda técnica trata do caso da extração e classificação de características faciais. Ela propõe um novo modelo para reconhecimento facial através do uso de características extraídas por Histogramas Census e de uma técnica de reconhecimento de padrões baseada em Máquinas de Vetores de Suporte (SVMs). Este outro grupo de experimentos nos possibilitou aumentar a precisão do reconhecimento no teste FERET fa/fb em 0.5%. Além destes resultados...

‣ Learning person-specific face representations = : Aprendendo representações específicas para a face de cada pessoa; Aprendendo representações específicas para a face de cada pessoa

Giovani Chiachia
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 27/08/2013 Português
Relevância na Pesquisa
78.33793%
Os seres humanos são especialistas natos em reconhecimento de faces, com habilidades que excedem em muito as dos métodos automatizados vigentes, especialmente em cenários não controlados, onde não há a necessidade de colaboração por parte do indivíduo sendo reconhecido. No entanto, uma característica marcante do reconhecimento de face humano é que nós somos substancialmente melhores no reconhecimento de faces familiares, provavelmente porque somos capazes de consolidar uma grande quantidade de experiência prévia com a aparência de certo indivíduo e de fazer uso efetivo dessa experiência para nos ajudar no reconhecimento futuro. De fato, pesquisadores em psicologia têm até mesmo sugeridos que a representação interna que fazemos das faces pode ser parcialmente adaptada ou otimizada para rostos familiares. Enquanto isso, a situação análoga no reconhecimento facial automatizado | onde um grande número de exemplos de treinamento de um indivíduo está disponível | tem sido muito pouco explorada, apesar da crescente relevância dessa abordagem na era das mídias sociais. Inspirados nessas observações, nesta tese propomos uma abordagem em que a representação da face de cada pessoa é explicitamente adaptada e realçada com o intuito de reconhecê-la melhor. Apresentamos uma coleção de métodos de aprendizado que endereça e progressivamente justifica tal abordagem. Ao aprender e operar com representações específicas para face de cada pessoa...

‣ A novel polar-based human face recognition computational model

Zana,Y.; Mena-Chalco,J.P.; Cesar Jr.,R.M.
Fonte: Associação Brasileira de Divulgação Científica Publicador: Associação Brasileira de Divulgação Científica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/07/2009 Português
Relevância na Pesquisa
68.13753%
Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns...

‣ Proposing the novelty classifier for face recognition

Costa Filho,Cicero Ferreira Fernandes; Falcão,Thiago de Azevedo; Costa,Marly Guimarães Fernandes; Pereira,José Raimundo Gomes
Fonte: SBEB - Sociedade Brasileira de Engenharia Biomédica Publicador: SBEB - Sociedade Brasileira de Engenharia Biomédica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2014 Português
Relevância na Pesquisa
68.04412%
INTRODUCTION: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition: the novelty classifier. METHODS: The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. RESULTS: In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%. CONCLUSION: For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.

‣ An Incremental Multilinear System for Human Face Learning and Recognition

Wang, Jin
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Português
Relevância na Pesquisa
67.985723%
This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research...

‣ Comparação entre algoritmos de reconhecimento de face no contexto de acessibilidade; Comparison between face recognition algorithms in acessibility context

Douglas Eduardo Parra
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/06/2014 Português
Relevância na Pesquisa
78.21334%
Nesta dissertação de mestrado, é mostrada uma comparação entre três algoritmos de reconhecimento de face no contexto de acessibilidade para o projeto Microsoft com parceria com a FAPESP, para o módulo de reconhecimento de pessoas utilizando o Microsoft Kinect e substituição sensorial. O algoritmo k-Nearest Neighbours, junto do descritor Histograma de Gradientes Orientados, foi utilizado como base por ser uma abordagem simples e de baixo custo computacional. Os algoritmos Eigenfaces e Local Binary Pattern Histogram foram comparados com o anterior em quatro experimentos. Inicialmente, é descrito o Projeto Vision for the Blind e seus diversos módulos. Este projeto foi desenvolvido por uma equipe aqui no Brasil, que obteve bons resultados para os módulos de navegação e reconhecimento de face, sempre com a ideia de usar o áudio 3D para passar a informação desejada ao usuário. Em seguida, é apresentada uma revisão do estado da arte com projetos no contexto de acessibilidade e substituição sensorial, apontando suas limitações. Logo após é feita uma revisão sobre os três algoritmos de reconhecimento facial utilizados e, então, como foi construída o banco de imagens deste projeto. Foram obtidos bons resultados com os três algoritmos...

‣ Capturing Specific Abilities as a Window into Human Individuality: The Example of Face Recognition

Wilmer, Jeremy Bennet; Germine, Laura Thi; Chabris, Christopher; Chatterjee, Garga; Gerbasi, Margaret E; Nakayama, Ken
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
78.38913%
Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology...

‣ Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count

Castillo, Luis E.; Cament, Leonardo A.; Pérez, Claudio A.
Fonte: Elsevier Publicador: Elsevier
Tipo: Artículo de revista
Português
Relevância na Pesquisa
68.140903%
Artículo de publicación ISI; Face recognition has a wide range of possible applications in surveillance, human computer interfaces and marketing and advertising goods for selected customers according to age and gender. Because of the high classification rate and reduced computational time, one of the best methods for face recognition is based on Gabor jet feature extraction and Borda count classification. In this paper, we propose methodological improvements to increase face recognition rate by selection of Gabor jets using entropy and genetic algorithms. This selection of jets additionally allows faster processing for real-time face recognition. We also propose improvements in the Borda count classification through a weighted Borda count and a threshold to eliminate low score jets from the voting process to increase the face recognition rate. Combinations of Gabor jet selection and Borda count improvements are also proposed. We compare our results with those published in the literature to date and find significant improvements. Our best results on the FERET database are 99.8%, 99.5%, 89.2% and 86.8% recognition rates on the subsets Fb, Fc, Dup1 and Dup2, respectively. Compared to the best results published in the literature, the total number of recognition errors decreased from 163 to 112 (31%). We also tested the proposed method under illumination changes...

‣ A Parallel Framework for Multilayer Perceptron for Human Face Recognition

Bhowmik, M. K.; Bhattacharjee, Debotosh; Nasipuri, M.; Basu, D. K.; Kundu, M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/07/2010 Português
Relevância na Pesquisa
78.158047%
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Both the structures were implemented and tested for face recognition purpose and experimental results show that the OCON structure performs better than the generally used ACON ones in term of training convergence speed of the network. Unlike the conventional sequential approach of training the neural networks...

‣ Minutiae Based Thermal Face Recognition using Blood Perfusion Data

Seal, Ayan; Nasipuri, Mita; Bhattacharjee, Debotosh; Basu, Dipak Kumar
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/09/2013 Português
Relevância na Pesquisa
68.31444%
This paper describes an efficient approach for human face recognition based on blood perfusion data from infra-red face images. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to optical image data. Blood perfusion data are related to distribution of blood vessels under the face skin. A distribution of blood vessels are unique for each person and as a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. For classification, a five layer feed-forward backpropagation neural network has been used. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block sizes. Experiments have been performed on the database created at our own laboratory. The maximum success of 91.47% recognition has been achieved with block size 8X8.; Comment: 4 pages...

‣ Automated Thermal Face recognition based on Minutiae Extraction

Seal, Ayan; Ganguly, Suranjan; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kr.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/09/2013 Português
Relevância na Pesquisa
68.187856%
In this paper an efficient approach for human face recognition based on the use of minutiae points in thermal face image is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted. Blood perfusion data are related to distribution of blood vessels under the face skin. In the present work, three different methods have been used to get the blood perfusion image, namely bit-plane slicing and medial axis transform, morphological erosion and medial axis transform, sobel edge operators. Distribution of blood vessels is unique for each person and a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. Two different methods are discussed for extracting minutiae points from blood perfusion data. For extraction of features entire face image is partitioned into equal size blocks and the total number of minutiae points from each block is computed to construct final feature vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition methodologies with varying block size on the database created at our own laboratory. It has been found that the first method supercedes the other two producing an accuracy of 97.62% with block size 16X16 for bit-plane 4.; Comment: 29 pages...

‣ Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

Seal, Ayan; Ganguly, Suranjan; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/09/2013 Português
Relevância na Pesquisa
68.069424%
In this paper, a thermal infra red face recognition system for human identification and verification using blood perfusion data and back propagation feed forward neural network is proposed. The system consists of three steps. At the very first step face region is cropped from the colour 24-bit input images. Secondly face features are extracted from the croped region, which will be taken as the input of the back propagation feed forward neural network in the third step and classification and recognition is carried out. The proposed approaches are tested on a number of human thermal infra red face images created at our own laboratory. Experimental results reveal the higher degree performance; Comment: 7 pages, Conference. arXiv admin note: substantial text overlap with arXiv:1309.1000, arXiv:1309.0999, arXiv:1309.1009

‣ Thermal Human face recognition based on Haar wavelet transform and series matching technique

Seal, Ayan; Ganguly, Suranjan; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak kr.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/09/2013 Português
Relevância na Pesquisa
78.274175%
Thermal infrared (IR) images represent the heat patterns emitted from hot object and they do not consider the energies reflected from an object. Objects living or non-living emit different amounts of IR energy according to their body temperature and characteristics. Humans are homoeothermic and hence capable of maintaining constant temperature under different surrounding temperature. Face recognition from thermal (IR) images should focus on changes of temperature on facial blood vessels. These temperature changes can be regarded as texture features of images and wavelet transform is a very good tool to analyze multi-scale and multi-directional texture. Wavelet transform is also used for image dimensionality reduction, by removing redundancies and preserving original features of the image. The sizes of the facial images are normally large. So, the wavelet transform is used before image similarity is measured. Therefore this paper describes an efficient approach of human face recognition based on wavelet transform from thermal IR images. The system consists of three steps. At the very first step, human thermal IR face image is preprocessed and the face region is only cropped from the entire image. Secondly, Haar wavelet is used to extract low frequency band from the cropped face region. Lastly...

‣ Real time face recognition using adaboost improved fast PCA algorithm

Kumar, K. Susheel; Semwal, Vijay Bhaskar; Tripathi, R C
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/08/2011 Português
Relevância na Pesquisa
68.19333%
This paper presents an automated system for human face recognition in a real time background world for a large homemade dataset of persons face. The task is very difficult as the real time background subtraction in an image is still a challenge. Addition to this there is a huge variation in human face image in terms of size, pose and expression. The system proposed collapses most of this variance. To detect real time human face AdaBoost with Haar cascade is used and a simple fast PCA and LDA is used to recognize the faces detected. The matched face is then used to mark attendance in the laboratory, in our case. This biometric system is a real time attendance system based on the human face recognition with a simple and fast algorithms and gaining a high accuracy rate..; Comment: 14 pages; ISSN : 0975-900X (Online), 0976-2191 (Print)

‣ Next Level of Data Fusion for Human Face Recognition

Bhowmik, Mrinal Kanti; Majumdar, Gautam; Bhattacharjee, Debotosh; Basu, Dipak Kumar; Nasipuri, Mita
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/06/2011 Português
Relevância na Pesquisa
77.84396%
This paper demonstrates two different fusion techniques at two different levels of a human face recognition process. The first one is called data fusion at lower level and the second one is the decision fusion towards the end of the recognition process. At first a data fusion is applied on visual and corresponding thermal images to generate fused image. Data fusion is implemented in the wavelet domain after decomposing the images through Daubechies wavelet coefficients (db2). During the data fusion maximum of approximate and other three details coefficients are merged together. After that Principle Component Analysis (PCA) is applied over the fused coefficients and finally two different artificial neural networks namely Multilayer Perceptron(MLP) and Radial Basis Function(RBF) networks have been used separately to classify the images. After that, for decision fusion based decisions from both the classifiers are combined together using Bayesian formulation. For experiments, IRIS thermal/visible Face Database has been used. Experimental results show that the performance of multiple classifier system along with decision fusion works well over the single classifier system.; Comment: Keywords: Thermal Image, Visual Image, Fused Image, Data Fusion...

‣ Face recognition with variation in pose angle using face graphs

Kumar, Sooraj
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
78.36483%
Automatic recognition of human faces is an important and growing field. Several real-world applications have started to rely on the accuracy of computer-based face recognition systems for their own performance in terms of efficiency, safety and reliability. Many algorithms have already been established in terms of frontal face recognition, where the person to be recognized is looking directly at the camera. More recently, methods for non-frontal face recognition have been proposed. These include work related to 3D rigid face models, component-based 3D morphable models, eigenfaces and elastic bunched graph matching (EBGM). This thesis extends recognition algorithm based on EBGM to establish better face recognition across pose variation. Facial features are localized using active shape models and face recognition is based on elastic bunch graph matching. Recognition is performed by comparing feature descriptors based on Gabor wavelets for various orientations and scales, called jets. Two novel recognition schemes, feature weighting and jet-mapping, are proposed for improved performance of the base scheme, and a combination of the two schemes is considered as a further enhancement. The improvements in performance have been evaluated by studying recognition rates on an existing database and comparing the results with the base recognition scheme over which the schemes have been developed. Improvement of up to 20% has been observed for face pose variation as large as 45°.

‣ Face recognition in low resolution video sequences using super resolution

Arachchige, Somi Ruwan Budhagoda
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
78.390596%
Human activity is a major concern in a wide variety of applications, such as video surveillance, human computer interface and face image database management. Detecting and recognizing faces is a crucial step in these applications. Furthermore, major advancements and initiatives in security applications in the past years have propelled face recognition technology into the spotlight. The performance of existing face recognition systems declines significantly if the resolution of the face image falls below a certain level. This is especially critical in surveillance imagery where often, due to many reasons, only low-resolution video of faces is available. If these low-resolution images are passed to a face recognition system, the performance is usually unacceptable. Hence, resolution plays a key role in face recognition systems. In this thesis, we address this issue by using super-resolution techniques as a middle step, where multiple low resolution face image frames are used to obtain a high-resolution face image for improved recognition rates. Two different techniques based on frequency and spatial domains were utilized in super resolution image enhancement. In this thesis, we apply super resolution to both images and video utilizing these techniques and we employ principal component analysis for face matching...