Facial expression recognition is a challenging task, arguably because of large intra-class variations and high inter-class similarities. The core drawback of the existing approaches is the lack of ability to discriminate the changes in appearance caused by emotions and identities. In this paper, we present a novel identity-enhanced network (IDEnNet) to eliminate the negative impact of identity factor and focus on recognizing facial expressions. Spatial fusion combined with self-constrained multi-task learning are adopted to jointly learn the expression representations and identity-related information. We evaluate our approach on three popular datasets, namely Oulu-CASIA, CK+ and MMI. IDEnNet improves the baseline consistently, and achieves the best or comparable state-of-the-art on all three datasets.
@article{arxiv.1812.04207,
title = {Identity-Enhanced Network for Facial Expression Recognition},
author = {Yanwei Li and Xingang Wang and Shilei Zhang and Lingxi Xie and Wenqi Wu and Hongyuan Yu and Zheng Zhu},
journal= {arXiv preprint arXiv:1812.04207},
year = {2018}
}