Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (1): 35-39.doi: 10.16180/j.cnki.issn1007-7820.2022.01.006

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Research on Occluded Face Recognition Method Based on Deep Learning

CHENG Xiaoya,ZHANG Lei   

  1. Maths and Information Technology School,Yuncheng University,Yuncheng 044000,China
  • Received:2021-01-18 Online:2022-01-15 Published:2022-02-24
  • Supported by:
    Science and Technology Innovation Project of Colleges and Universities in Shanxi(2019L0855);Scientific Research Project of Yuncheng University(CY-2019035)


In view of the large amount of calculation in traditional CNN in occluded face recognition, this study proposes a new PCANet deep learning network for face recognition based on L1-2DPCA. The proposed network uses L1-2DPCA to learn filters of multiple convolutional layers. After the convolutional layer, pooling is performed through binary hashing and block-by-block histogram. CNN, PCANet, 2DPCANet and L1-PCANet are compared, and the proposed network is tested on AR and RMFD face data sets. The results show that in all tests, the recognition performance of the deep learning network proposed in this study is better than other networks. Due to the use of L1 norm, the proposed deep learning network has strong robustness to the changes of outliers and training images.

Key words: face recognition, occlusion, deep learning, L1-2DPCA, two-dimensional principal component analysis, L1 norm, CNN, robustness

CLC Number: 

  • TN432