Light Attention Embedding for Facial Expression Recognition

文献类型： 外文期刊

第一作者： Wang, Cong

作者： Wang, Cong;Xue, Jian;Lu, Ke;Yan, Yanfu;Wang, Cong;Lu, Ke

作者机构：

关键词： Face recognition; Feature extraction; Task analysis; Training; Faces; Computer architecture; Videos; Facial expression recognition; attention mechanism; deep neural network

期刊名称：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY （ 影响因子：5.859； 五年影响因子：5.283 ）

ISSN： 1051-8215

年卷期： 2022 年 32 卷 4 期

页码：

收录情况： SCI

摘要： Facial expression recognition is important for human-computer interaction and other applications. Several facial expression datasets have been published in recent decades and have enabled improvements in algorithms for classifying emotions. However, recognition of realistic expressions in real-world conditions is still challenging because of uncontrolled conditions, such as lighting, brightness, pose, and occlusion. In this paper, we propose a light attention embedding network based on the spatial attention mechanism (LAENet-SA), which can focus on locations in an image that are relevant to emotion. LAENet-SA allows a small number of attention modules to be embedded and can be constructed from typical convolutional neural networks. The performance of LAENet-SA on facial expression recognition has been validated on three facial expression datasets, including a lab-controlled dataset and two in-the-wild datasets. Experimental results show that LAENet-SA improved the performance on each dataset, compared with state-of-the-art methods, and achieved better generalization when tested on facial images with occlusion.

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