Facial expression recognition based on convolutional block attention module and multi-feature fusion. (27th October 2022)

• Record Type: Journal Article
• Authors: Jiang, Man; Yin, Shoulin
• Is Part Of: International journal of computational vision and robotics. Volume 13:Number 1(2023)
• Page Start: 21
• Page End: 37
• Publication Date: 2022-10-27
• Subjects: facial expression recognition -- convolutional block attention module -- CBAM -- multi-feature fusion -- local feature clustering -- LFC; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

In this paper, we focus on the research of facial expression recognition. A novel convolutional block attention module and multi-feature fusion method are proposed for facial expression recognition. The local feature clustering loss function is proposed, which can reduce the difference between the same classes of images and enlarge the difference between different classes of images in the training process. The convolutional block attention module is adopted to better express facial expressions in local areas with rich expressions. Experimental results show that the proposed method can effectively recognise different expressions on the RAF dataset and CK+ dataset compared with other state-of-the-art methods.