A dual stream attention network for facial expression recognition in the wild

Facial Expression Recognition (FER) is crucial for human-computer interaction and has achieved satisfactory results on lab-collected datasets. However, occlusion and head pose variation in the real world make FER extremely challenging due to facial information deficiency. This paper proposes a novel Dual Stream Attention Network (DSAN) for occlusion and head pose robust FER. Specifically, DSAN consists of a Global Feature Element-based Attention Network (GFE-AN) and a Multi-Feature Fusion-based Attention Network (MFF-AN). A sparse attention block and a feature recalibration loss designed in GFE-AN selectively emphasize feature elements meaningful for facial expression and suppress those unrelated to facial expression. And a lightweight local feature attention block is customized in MFF-AN to extract rich semantic information from different representation sub-spaces. In addition, DSAN takes into account computation overhead minimization when designing model architecture. Extensive experiments on public benchmarks demonstrate that the proposed DSAN outperforms the state-of-the-art methods with 89.70% on RAF-DB, 89.93% on FERPlus, 65.77% on AffectNet-7, 62.13% on AffectNet-8. Moreover, the parameter size of DSAN is only 11.33M, which is lightweight compared to most of the recent in-the-wild FER algorithms.