To reduce dependence of individual on facial expression recognition

control scale of recognition dictionary

increase recognition accuracy

propose a dictionary recognition construction method based on collaborative low-rank and hierarchical sparse representation. Effective separation the relevant characteristics of the individual to be identified by collaboration lowvrank and hierarchical sparse model（ C-Hi SLR）

keep expression variation characteristics

combined with label consist KSVD（ LC-KSVD）

to identify and reconstruct the sequence of expression to be trained and optimization study of distinction between corresponding category dictionary. The methods is verified on data set in CK +

recognition effect is significantly improved than general model algorithm based on sparse representation.