A hierarchical algorithm with multi-feature fusion for facial expression recognition

In this paper, a novel hierarchical algorithm with multi-feature fusion is proposed for facial expression recognition. In this area, many people have proposed many good results, but few of them made good use of the distribution characteristic of facial expression itself. In the analysis of the feature distribution, we find happiness and surprise are clearly separated from the other expressions. So we aim to distinguish these two expressions in the first layer of our algorithm using Gabor features. In the second layer, we use Gabor and LBP features respectively to classify the other five expressions. And a well designed result fusion of two branches is adopted to improve the accuracy. Experiments results on the Cohn-Kanade database show that our algorithm achieves excellent accuracy. Furthermore, our algorithm also performs well in our hybrid database, in which there are extensive variations of expressions. It demonstrates the good generalization ability of our algorithm.