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Using a compact dictionary obtained by sparse learning could greatly improve the accuracy and speed up the procedure of classification for sparse representation-based face recognition method.However,the traditional metaface learning (MFL)method doesn’t take into account the similarity among the training samples from the same person.In order to take the advantage of this prior information and make the learned dictionary more discriminative,an algorithm caled coefficient-similarity-based metaface learning(CS-MFL)was proposed.In CS-MFI ,the coefficient similarity is incorporated as a new constraint to the original objective function.To solve the new optimization problem,both lz normbased constraints are combined,and the original problem becomes a typical 12一l l problem.An experiment was carried out on different face databases,which shows that the proposed CS-MFI algorithm can achieve higher recognition rate than MFL algorithm,which demonstrates that the dictionary obtained by CS-MFL algorithm is more efficient and dis— criminative than that of the traditional MFL for face recognition application.

人脸识别中基于系数相似性的字典学习算法 (Coefficient-similarity-based Dictionary Learning Algorithm for Face Recognition).