Facial Expression Recognition Based On Hierarchical Feature Learning

Facial expression recognition is a challenging problem in the field of computer vision. In this paper, we propose a deep learning approach that can learn the joint low-level and high-level features of human face to resolve this problem. Our deep neural networks utilize convolution and downsampling to extract the abstract and local features of human face, and reconstruct the raw input images to learn global features as supplementary information at the same time. We also add an adjustable weight in the networks when combining the two kinds of features for the final classification. The experimental results show that the proposed method can achieve good results, which has an average recognition accuracy of 93.65% on the test datasets.