Identity-Enhanced Network for Facial Expression Recognition
This work addresses facial expression recognition for computer vision applications, but it is incremental as it builds on existing methods to handle identity-related variations.
The paper tackled the problem of facial expression recognition by addressing the challenge of distinguishing between appearance changes due to emotions and identities, resulting in improved performance on three datasets with best or comparable state-of-the-art results.
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.