Hand-Crafted Feature Guided Deep Learning for Facial Expression Recognition

Abstract

A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.