Video-Based Face Recognition and Face-Tracking using Sparse Representation Based Categorization

Face recognition system is used in order to automatically identify a person from an image or a video source. The recognition task is performed by obtaining facial features from an image of the subject's face. The main objective of video-based face recognition is to identify a video face-track of famous personalities using a large dictionary of still face images, while rejecting unknown individuals Existing methods use probabilistic models on a frame-by-frame basis to identify faces which is computationally expensive when the data size is large. To overcome this drawback, the proposed regularized sparse representation classification (RSRC) algorithm uses £2 minimization approach instead of conventional £1 minimization method and obtains a single coefficient vector for all frames. Since second order minimization is used, more sparsity ratios are achieved and the residual error over the frames are reduced. The proposed algorithm is compared with the existing methods and the experimental results prove that, due to minimal error better classification accuracy and high confidence value are achieved.