Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment

Sparse Representation based Classification （SRC） has emerged as a new paradigm for solving recognition problems. This paper presents a constraint sampling feature extraction method that improves the SRC recognition rate. The method combines texture and shape features to significantly improve the recognition rate. Tests show that the combined constraint sampling and facial alignment achieves very high recognition accuracy on both the AR face database （99.52%） and the CAS-PEAL face database （99.54%）.