Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 29, 2020

Abstract

BACKGROUND AND OBJECTIVE: Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion.

Authors

  • Jialin Tang
    Beijing Institute of Technology, Zhuhai 519088, China; City University of Macau, Macau, China. Electronic address: thong03@qq.com.
  • Qinglang Su
    City University of Macau, Macau, China. Electronic address: sonnysu@cityu.mo.
  • Binghua Su
    Beijing Institute of Technology, Zhuhai 519088, China. Electronic address: 01004@bitzh.edu.cn.
  • Simon Fong
    University of Macau, Macau.
  • Wei Cao
    Collaborative Innovation Center for Green Chemical Manufacturing and Accurate Detection, Key Laboratory of Interfacial Reaction & Sensing Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, PR China.
  • Xueyuan Gong
    Beijing Institute of Technology, Zhuhai 519088, China. Electronic address: 18202@bitzh.edu.cn.

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