Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (8): 47-52.doi: 10.16180/j.cnki.issn1007-7820.2022.08.008

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Infant Expression Recognition Algorithm Based on MobileNetV2 and LBP Feature Fusion

DENG Yuan,SHI Yiping,JIANG Yueying,ZHU Yamei,LIU Jin   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science, Shanghai 201620,China
  • Received:2021-03-16 Online:2022-08-15 Published:2022-08-10
  • Supported by:
    National Natural Science Foundation of China(61701296);Graduate Science Innovation Project of Shanghai University of Engineering Science(20KY0218)


In view of the problems of low rate of infant expression recognition and the insufficient extraction of complex features, an infant expression recognition algorithm based on MobileNetV2 and LBP dual-channel feature fusion is proposed. The first channel uses the improved MobileNetV2 network to quickly and accurately extract the global features of facial expressions. The second channel divides the original input image into blocks, and uses image information entropy to construct weights, and extracts block-weighted LBP histogram features to highlight the regions with rich expression information. The output vector of the dual-channel model is fused to improve the feature expression ability, and the support vector machine is used to replace the Softmax layer for expression classification. Experiments show that the use of fusion features has a better classification effect than a single feature, and in the self-built infant expression data set, the accuracy of expression recognition can reach 85.71%.

Key words: convolutional neural network, local binary pattern, feature fusion, dual-channel model, expression recognition, image information entropy, infant, support vector machine

CLC Number: 

  • TP391