Sparse Representation Classification Algorithm Based on Mahalanobis Distance
Jianling Hu · Computer Technology and Development · 2011
In this paper a novel Mahalanobis Distance based method for sparse representation classification was designed to improve the recognition efficiency for different illumination condition face images.Mahalanobis Distance and Cholesky decomposition are introduced to solve the sparse solution vector,and Mahalanobis Distance based Sparse Representation Classification(MSRC) is designed to recognize the face image.Firstly,Mahalanobis Distance based L1-minimization algorithm is proposed to obtain the sparse representation.Then,reconstruct the test image.Finally,the one that has the minimum reconstruction error is selected as the most matched face.Compared to the traditional SRC algorithms,our algorithm significantly reduces the influence of illumination.Lots of numerical experiments based on ORL face database and Extended Yale face database B are performed.The results show that the proposed Mahalanobis Distance based Sparse Representation Classification algorithm can achieve about 97% recognition rate for normal face images.