Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (03): 10-16.doi: 10.12052/gdutxb.210179

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Facial Expression Recognition Based on Multi-feature Fusion

Lai Dong-sheng1, Feng Kai-ping2, Luo Li-hong2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Art and Design, Guangdong University of Technology, Guangzhou 510090, China
  • Received:2021-11-17 Online:2023-05-25 Published:2023-06-08

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Abstract: For the insufficient generalization ability of lightweight facial expression recognition algorithms, a facial expression recognition method based on multi-feature fusion and attention mechanism is proposed. LBP is used to reduce the interference of meaningless information of original face image, the dual-branch neural network extracts the features of original face image and LBP image respectively, merges the middle-level and high-level feature information extracted by these two networks and strengthens important features through attention mechanism for facial expression recognition, which can generate a large amount of discriminative features by multi-feature fusion to improve the recognition performance with a small number of parameters. The experimental results show that the recognition rate of this method has reached 70.21% on Fer2013 dataset and 95.59% on CK+ dataset respectively, which effectively improves the performance of lightweight expression recognition algorithm.

Key words: facial expression recognition, lightweight network, local binary pattern, multi-feature fusion, attention mechanism

CLC Number: 

  • TP391
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