Title: Facial expression recognition based on convolutional block attention module and multi-feature fusion
Authors: Man Jiang; Shoulin Yin
Addresses: Liaoning Vocational Technical College of Modern Service, Shenyang 110034, China ' Software College, Shenyang Normal University, Shenyang 110034, China
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.
Keywords: facial expression recognition; convolutional block attention module; CBAM; multi-feature fusion; local feature clustering; LFC.
International Journal of Computational Vision and Robotics, 2023 Vol.13 No.1, pp.21 - 37
Received: 19 Nov 2021
Accepted: 03 Dec 2021
Published online: 30 Nov 2022 *