Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (4): 18-22.doi: 10.16180/j.cnki.issn1007-7820.2020.04.004

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Face Recognition Algorithm Based on Deep Learning and Feature Fusion

SI Qin,LI Feifei,CHEN Qiu   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-03-01 Online:2020-04-15 Published:2020-04-23
  • Supported by:
    Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning(ES2015XX)


The convolutional neural network performed well on face recognition, but the extracted facial feature ignores the local structure features of the face. In order to address this problem, a novel method was proposed, which was based on deep learning and feature fusion. This algorithm made the extracted facial features more comprehensive by using a combination of local binary pattern information and original image information as the input of SDFVGG network, which was a neuralnetwork fusing shallow and deep features of VGG network. Experimental results on the CAS-PEAL-R1 face database demonstrated that the proposed algorithm was very effective for improving the accuracy of face recognition, and achieved a maximum face recognition rate of 98.58% which was better than traditional algorithms and general convolutional neural networks.

Key words: feature extraction, feature fusion, convolutional neural network, SDFVGG, local binary pattern, face recognition

CLC Number: 

  • TP391