Abstract
By coding the input testing image as a sparse linear combination of the training samples via L1-norm minimization, sparse representation based classification (SRC) has been successfully employed for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to sparsely code the occluded portions in face images, SRC can lead to robust FR results against occlusion. However, it has not been evaluated in applications with respect to efficiency and image variety. In the proposed system, we have integrated face detection and the sparse presentation for online face recognition, in stead of training and testing face images. The performance of sparse representation has been evaluated in image sequences with the integrated recognition system.
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