Bayesian Face Recognition Approach Based on Feature Fusion

  • Jingjing Liu
  • Donghui He
  • Xiaoyang ZengEmail author
  • Mingyu WangEmail author
  • Xianchao Xiu
  • Wanquan Liu
  • Hui Chen
  • Yuyao Xiao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Feature extraction and matching recognition are two critical stages in face recognition process. While traditional Bayesian classifier exists the small sample problem in the matching recognition stage, a novel Bayesian face recognition approach based on feature fusion is proposed. In the feature extraction stage, the global non-linear feature is extracted by kernel principal component analysis (KPCA), and the local manifold structure information is extracted by the orthogonal locality sensitive discriminant analysis (OLSDA), achieving the purpose of extracting the low-dimension essential facial feature with high-discrimination, and the constraints of the fusion features make the obtained matrix be closer to the desired solution. In the matching recognition stage, a maximum entropy covariance selection (MECS) method is utilized to solve the small sample problem. Extensive experimental results on several datasets show that these two stages can significantly improve the accuracy of face recognition.


Face recognition Kernel principal component analysis Feature fusion Bayesian classifier 



This work was partially supported by the National Natural Science Foundation of China with Grant Nos. (61525401, 61234002, 51705304), Natural Science Foundation of Shanghai with Grant Nos. (19ZR1420800, 16ZR1413400), the Program of Shanghai Academic/Technology Research Leader with Grant No. 16XD1400300, the Major Scientific and Technological Innovation Projects of Shanghai Education Commission with Grant No. 2017-01-07-00-07-E00026.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jingjing Liu
    • 1
  • Donghui He
    • 1
  • Xiaoyang Zeng
    • 1
    Email author
  • Mingyu Wang
    • 1
    Email author
  • Xianchao Xiu
    • 2
  • Wanquan Liu
    • 3
  • Hui Chen
    • 4
  • Yuyao Xiao
    • 5
  1. 1.Fudan UniversityShanghaiPeople’s Republic of China
  2. 2.Peking UniversityBeijingChina
  3. 3.Curtin UniversityPerthAustralia
  4. 4.Shanghai University of Electric PowerShanghaiPeople’s Republic of China
  5. 5.Beijing Institute of TechnologyBeijingPeople’s Republic of China

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