Self-adaptive Feature Fusion Method for Improving LBP for Face Identification

Xin Wei, Hui Wang, Huan Wan, Bryan Scotney

Research output: Contribution to conferencePaperpeer-review


In a recent paper, a multi-scale information fusion method was presented to improve LBP for face identification. However, the additional parameters employed in that method cannot be automatically optimised. In this paper, a novel self-adaptive feature fusion method is proposed which extends the mLBP method by removing the need to optimise these parameters. Our method involves four steps. Firstly, a large number of initial features are generated. Then, we proposed a Fisher criteria-based method for evaluating the discriminative capabilities of different feature groups. After that, we proposed a model based on prism volume for selecting the optimal parameter combination. Finally, the resulting multi-scale feature are fused by a extended Euclidean distance fusion. Extensive experiments on two face databases have shown the proposed self-adaptive feature fusion method can find parameters that are optimal to the data in question, and can produce excellent classification performance.
Original languageEnglish
Publication statusPublished online - 11 Oct 2017


  • Self-adaptive
  • Feature fusion
  • Face identification
  • LBP


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