Facial Expression Recognition Combining Self-Attention Feature Filtering Classifier and Two-Branch GAN
CHENG Yan1,2, CAI Zhuang1, WU Gang1, LUO Pin1, ZOU Haifeng1
1. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022; 2. Jiangxi Provincial Key Laboratory of Intelligent Education, Science and Technology Department of Jiangxi Province, Nan-chang 330022
Abstract：The expression features extracted by the existing facial expression recognition methods are usually mixed with other facial attributes, which is not conducive to facial expression recognition. A facial expression recognition model combining self-attention feature filter classifier and two-branch generative adversarial network is proposed. Two-branch generative adversarial network is introduced to learn discriminative expression representation, and a self-attention feature filtering classifier is proposed as the expression classification module. The cascaded LayerNorm and ReLU are employed to zero the low activation unit and retain the high activation unit to generate multi-level features. The self-attention is utilized to fuse and output the prediction results of multi-level features, and consequently the influence of noise on the recognition results is eliminated to a certain extent. A sliding module based dual image consistency loss supervised model is proposed to learn discriminative expression representations. The reconstruction loss is calculated by a sliding window and more attention is paid to the details. Finally, experiments on CK+, RAF-DB, TFEID and BAUM-2i datasets show the proposed model achieves better recognition results.
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2022, Vol. 35 
