# Identity-Enhanced Network for Facial Expression Recognition

## TL;DR

This paper introduces IDEnNet, a novel neural network that enhances facial expression recognition by reducing identity-related interference through spatial fusion and multi-task learning, achieving state-of-the-art results.

## Contribution

The paper proposes a new identity-enhanced network with spatial fusion and multi-task learning to better discriminate expressions from identities.

## Key findings

- Consistently improves baseline performance.
- Achieves best or comparable state-of-the-art on three datasets.
- Effectively reduces identity interference in expression recognition.

## Abstract

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.