Abstract:

Face recognition problem based on sparse representation attempts to obtain a dictionary with both good represent power and effective discriminative ability. The discriminative KSVD algorithm (Dksvd) based on sparse representation is a dictionary training method which satisfies the above requirement jointly. However, the initialized dictionary of the Dksvd algorithm is trained from some sample selected from the training data using KSVD, which cannot represent the training data completely, and increases the residual of the initialization dictionary. The face recognition rate will be affected by the aforementioned problem. The algorithm proposed in this paper improves the initialization method of Dksvd algorithm. The dictionaries are trained in every category and join together to form a new initialized dictionary. Every learned dictionary is the optimized dictionary in each category, which decreases the residual of the trained dictionary, and increases the discriminative ability of the trained dictionary and the linear classifier. The face recognition rate is increased and the average recognition speed is fast.

Key words: face recognition;improved Dksvd;sparse representation;dictionary training