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Automatic face recognition is a classic problem in the area of computer vision research. This problem is still a very active area of research in vision community. The primary reason for this problem to get so much attention is the fact that face recognition finds application in many commercial applications and can work as a biometric in many law enforcement applications [1]. The problem of Automatic face recognition can be formally defined as follows: Given a set of representative training images for each person in the database, determine the identity of a new face images from the stored data. There have been several techniques proposed in literature to extract different type of features related to shape, color, etc. of the face. Some of the techniques simply use the image pixel values as the features and reduce the dimension of these features by applying some constraints such that classifcation property of training image is preserved.In this report, we discuss some of these methods which are widely used in literature and promise to exhibits good recognition accuracy. We will use Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Sparse representation and Random projection for the task of face recognition and study their advantages and disadvantages. |
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