A Survey: Face Recognition by Sparse Representation

Face recognition is very helpful in many applications such as video surveillance, forensic applications criminal investigations, and in many other fields. The most common methods includes PCA approach based Eigenface, Linear Discriminant Analysis(LDA), Hidden Markov Model(HMM),DWT, geometry based and template matching approaches.In this paper we are using sparse representation approach to attain more robustness to variation in lighting, directions and expressions. This survey paper performs analysis on different approaches and factors affecting the face recognition

Face detection in curvelet domain

Face detection and face recognition are two techniques in the field of image processing which have undergone significant research during the past few years. It stems from the availability of powerful algorithms, hardware and the wide range of applications they have got. Though powerful hardware and algorithms are available, today's face detection systems are far from perfect since they work within certain constraints. The performance of a face detection system is affected by the factors such as illumination, pose, occlusion etc. An efficient algorithm is the one which takes into account all the above factors, which would in turn increase the time complexity. Since time complexity acts as the bottleneck, development of an algorithm which detects face in minimum time is the need of the hour. Computations will take less time if a sparse representation can be provided for the image. Curvelet transform is an analysis tool which has the ability to sparsely represent images with curve discontinuities. In this work, curvelet transform is studied and is used to represent face images. Principal component analysis is done on this representation to reduce the dimension of the data. Euclidean distance is the parameter used to classify the face from non-faces. The performance of the system is analyzed using receiver operating characteristics (ROC)

Representação Esparsa e Modelo de Esparsidade Conjunta no Reconhecimento de Faces

Resumo: O trabalho desenvolvido nesta dissertação propõe a utilização do modelo de esparsidade conjunta com complemento de matrizes (JSM-MC) para composição da base de treino no contexto de reconhecimento de faces utilizando o classificador baseado em representação esparsa (SRC). O método proposto visa trabalhar com imagens de faces em diferentes condições de iluminação e oclusão na base de teste e treino. Para oclusões nas imagens de teste, um modelo diferenciado é considerado para abordar o problema. Uma etapa de pré-processamento nas imagens de faces é realizada no intuito de reduzir os efeitos das variações de iluminações presentes nas imagens. Um agrupamento das imagens de treino é realizado visando um menor tempo de processamento. Além disso, uma proposta de modificação no algoritmo SRC é feita de forma a explorar a esparsidade dos coeficientes de representação esparsa. Ao final, os resultados são avaliados usando uma base de dados sujeita a variação de iluminação. Oclusões artificiais são inseridas a fim de investigar o desempenho do sistema nessas condições