Multi-pose 3D face recognition based on joint sparse representation

A multi-pose 3D face recognition method based on joint sparse representation, called Joint Sparse Representation-based Classification (JSRC), is proposed in this paper. Multi-view 3D face test data are jointed for identity recognition by the hypothesis of multi test data joining the same sparse pattern. Consequently, we, using the JSRC method, construct 3D overcomplete dictionary and sparse representation model, thus completing the joint reconstruction of the sparse representation vector. The most notable advantage of the JSRC method is: utilizing the correlation of multi-view face, reducing the error identification risk of the traditional methods which consider only one test face each time, and improving the recognition accuracy. Experimental results on FRGC2.0 database and their analysis show preliminarily that JSRC method has higher performance in multi-pose 3D face recognition as compared with those obtainable respectively with mutual subspace method and sparse representation-based classification method.