In order to improve the performance of facial expression recognition by extracting the effective local features and global features

an algorithm for facial expression recognition based on adaptively weighted compound local binary pattern (AWCLBP) is proposed. First

the facial expression sub-regions are isolated by a preprocessing step. Then

the contribution maps(CM)of facial expression sub-regions are computed; Second

a compound local binary pattern(CLBP) extracts expression sub-regions and the global facial expression image and then cascade histograms are generated by connecting the histograms of the three features of the image and the one expression sub-regions that is weighted according to the CMs. Finally

the weighted cascade histograms are classified and recognized by using the chi-square distance and the nearest neighbor method. Experiment results on the facial expression database of JAFFE show that the proposed algorithm can be applied to achieve a higher recognition rate than other algorithms

such as

LBP

Gabor wavelet and the active appearance model.