2018/12/11 by Yanwei Li, Xingang Wang, Li, Yanwei +11
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1812.04207
arxiv created 2018/12/11 · openalex publication_date 2018/12/11 · arxiv updated 2018/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.