Boosting local binary pattern (LBP)-Based face recognition

  • Authors:
  • Guangcheng Zhang;Xiangsheng Huang;Stan Z. Li;Yangsheng Wang;Xihong Wu

  • Affiliations:
  • Center of Information Science, Peking University, Beijing, China;Institute of Automation, Academy of Chinese Sciences, Beijing, China;Institute of Automation, Academy of Chinese Sciences, Beijing, China;Institute of Automation, Academy of Chinese Sciences, Beijing, China;Center of Information Science, Peking University, Beijing, China

  • Venue:
  • SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
  • Year:
  • 2004

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Abstract

This paper presents a novel approach for face recognition by boosting statistical local features based classifiers The face image is scanned with a scalable sub-window from which the Local Binary Pattern (LBP) histograms [14] are obtained to describe the local features of a face image The multi-class problem of face recognition is transformed into a two-class one by classifying every two face images as intra-personal or extra-personal ones [9] The Chi square distance between corresponding Local Binary Pattern histograms of two face images is used as discriminative feature for intra/extra-personal classification We use AdaBoost algorithm to learn a similarity of every face image pairs The proposed method was tested on the FERET FA/FB image sets and yielded an exciting recognition rate of 97.9%.