MViT: Mask Vision Transformer for Facial Expression Recognition in the wild

• URL: http://arxiv.org/abs/2106.04520v1
• Date: Tue, 8 Jun 2021 16:58:10 GMT
• Title: MViT: Mask Vision Transformer for Facial Expression Recognition in the wild
• Authors: Hanting Li, Mingzhe Sui, Feng Zhao, Zhengjun Zha, and Feng Wu
• Abstract summary: Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision. In this work, we first propose a novel pure transformer-based mask vision transformer (MViT) for FER in the wild. Our MViT outperforms state-of-the-art methods on RAF-DB with 88.62%, FERPlus with 89.22%, and AffectNet-7 with 64.57%, respectively, and achieves a comparable result on AffectNet-8 with 61.40%.
• Score: 77.44854719772702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of annotators. These uncertainties make it difficult for neural networks to learn robust features on limited-scale datasets. Moreover, the networks can be easily distributed by the above factors and perform incorrect decisions. Recently, vision transformer (ViT) and data-efficient image transformers (DeiT) present their significant performance in traditional classification tasks. The self-attention mechanism makes transformers obtain a global receptive field in the first layer which dramatically enhances the feature extraction capability. In this work, we first propose a novel pure transformer-based mask vision transformer (MViT) for FER in the wild, which consists of two modules: a transformer-based mask generation network (MGN) to generate a mask that can filter out complex backgrounds and occlusion of face images, and a dynamic relabeling module to rectify incorrect labels in FER datasets in the wild. Extensive experimental results demonstrate that our MViT outperforms state-of-the-art methods on RAF-DB with 88.62%, FERPlus with 89.22%, and AffectNet-7 with 64.57%, respectively, and achieves a comparable result on AffectNet-8 with 61.40%.

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