Abstract: Aiming at the problem of face recognition under complicated conditions such as noise pollution and illumination changes, 
a face recognition algorithm based on improved label consistent KSVD dictionary learning is proposed. The algorithm changes the dictionary update way of the label consistent KSVD algorithm, uses the principal component analysis algorithm to decompose the error term, and the dictionary atom is modified by the eigenvector corresponding to the maximum eigenvalue. Then, through the dictionary learning process, the discriminative dictionary corresponding to atoms and category labels is obtained. The objective function combines reconstruction error, sparse coding error and classification error. Finally, in the classification stage, the learned dictionary and classifier parameters are used to classify the test samples. The average recognition rates of 99.01% and 97.94% are obtained on extended Yale B database with illumination conditions, and AR database with various facial expressions and occlusion effect, respectively. At the same time, the algorithm is more robust in the presence of noise. 

Key words: face recognition；label consistent KSVD algorithm；dictionary learning, principal component analysis