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Article type: Research Article
Authors: Baffour, Adu Asarea | Qin, Zhenb; * | Zhu, Guobinb | Ding, Yib | Qin, Zhiguangb
Affiliations: [a] School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R.China | [b] Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R.China
Correspondence: [*] Corresponding author. Zhen Qin, Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R.China. E-mail: [email protected].
Abstract: Recognizing facial expressions rely on facial parts’ movement (action units) such as eyes, mouth, and nose. Existing methods utilize complex subnetworks to learn part-based facial features or train neural networks with an extensively perturbed dataset. Different from existing methods, we propose a trainable end-to-end convolutional neural network for facial expression recognition. First, we propose a Local Prediction Penalty to stimulate facial expression recognition research with no part-based learning. It is a technique to punish the feature extractor’s local predictive power to coerce it to learn coarse-grained features (general facial expression). The Local Prediction Penalty forces the network to disregard predictive local signals learned from local receptive fields and instead depend on the global facial region. Second, we propose a Spatial Self-Attention method for fine-grained feature representation to learn distinct face features from pixel positions. The Spatial Self-Attention accumulates attention features at privileged positions without changing the spatial feature dimension. Lastly, we leverage a classifier to carefully combine all learned features (coarse-grained and fine-grained) for better feature representation. Extensive experiments demonstrate that our proposed methods significantly improve facial expression recognition performance.
Keywords: Facial expression recognition, spatial self-attention, coarse-grained, fine-grained, convolutional neural network
DOI: 10.3233/JIFS-212022
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3947-3959, 2022
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