Face Recognition Using Sparse Representation Classifier

Face is a biometric. Face recognition finds wide application in the identification, security, safety, etc. but the natural use of face recognition technology is the replacement of security password. The previous algorithms used for recognition purpose like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Support Vector Machine (SVM) has a problem like few feature extraction, poor accuracy result and so on. Hence, these algorithms are not 100% accurate for the face recognition purpose. So, because of this problem we obtain a new approach called Sparse Representation Classifier (SRC) for the identification of face. In the training phase, when we are applying a face to the detector, for each face a grid of patches is find out from each face in order to obtain a database for the recognition. After that, in the testing phase a grid is extracted from the input image and patch of every image is converted in to a binary representation using the database and after that they creating extraction of the face. Sparse representation classifier is deals with large degree of variations in lighting, person’s movements and expressions. It also deals with different face size.