Cross-domain facial expression recognition via an intra-category common feature and inter-category Distinction feature fusion network

Abstract

Facial expression recognition is crucial for various human-robot interaction applications, which requires facial expression analysis having a broad generalization. However, existing researches focus on the recognition in databases containing a limited number of samples. In this paper, we propose a novel feature fusion network for facial expression recognition in a cross-domain manner in order to realize the facial expression recognition in extensive scenarios. The proposed network consists of an Intra-category Common feature representation (IC) channel and an Inter-category Distinction feature representation (ID) channel for facial expression representation, and finally combine learned features of the two channels for facial expression recognition in cross databases. The IC channel learns the common features of intra-category facial expressions, and the ID channel learns the characteristic features of different categories. We evaluate the proposed approach in various experiment settings for cross-domain recognition, and achieves the state-of-the-art performances. We also evaluate the proposed approach for expression recognition in single databases, and also obtains the outstanding performance in the CK+, MMI, SFEW and RAF databases.