Facial expression recognition based on discriminative dictionary learning

Sparse Representation Classification (SRC) performs well in facial expression recognition (FER). However, SRC based methods costs a lot to train large number of examples. Sparse coding based method will be favorable to tackle the large scale facial expression recognition. K-SVD is state of the art sparse coding method. Unfortunately, K-SVD lacks of discrimination capability for it only focus on the representational power. To cover these problems, we apply discriminative K-SVD (D-KSVD) algorithm on Gabor features for facial expression recognition. Comparing with K-SVD, D-KSVD is more effective for it unifies dictionary and classifiers. We construct comprehensive experiments to verify the proposed algorithm on facial expression database JAFFE. Experimental result indicates that the performance of D-KSVD algorithm on Gabor features is more effective than the baselines including SRC and K-SVD algorithms.