Facial Expression Recognition With Deeply-Supervised Attention Network
By Yingruo Fan, Victor O. K. Li, Jacqueline C. K. Lam. 2020
87 citations recorded by the source corpus.
Facial expression recognition (FER) is crucial for social communication. However, current studies present limitations when addressing facial expression difference due to demographic variation, such as race, gender, and age, etc. In this article, we first propose a deeply-supervised attention network (DSAN) to recognize human emotions based on facial images automatically. Based on DSAN, a two-stage training scheme is designed, taking full advantage of the race/gender/age-related information. In our DSAN framework, multi-scale features are leveraged to capture more discriminative information from the deep layers to the shallow layers. Furthermore, we adopt the attention block to highlight the essential local facial characteristics; it performs well when it is incorporated into the deeply-supervised framework. Finally, we combine the complementary characteristics of multiple convolutional layers in deeply-supervised manner and ensemble the intermediate predicted scores. Our experimental results have shown that our proposed framework can (i) effectively integrate demographic information in improving the performance of a variety of FER tasks, (ii) learn informative feature representations with a visual explanation by capturing the regions of interests (ROI), (iii) achieve superior performance for both the posed and the spontaneous FER databases, each containing pictures of human facial expressions varied in gender, age or race.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
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Citations in the corpus
- Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution (2020, result): “Thus, compared to FER (Fan, Lam, and Li 2018b; Fan, Li, and Lam 2020) and AU recognition tasks (Li et al., 2019), discerning different intensities of AUs remains a far more challenging task.”
- Facial Expression Recognition With Visual Transformers and Attentional Selective Fusion (2021, methodology): “It isworthmentioning that Fan[24] first proposed a deeply-supervised attention network,which takes facial attributes into consideration.”