Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor

  • Authors:

  • Baochang Zhang;Yongsheng Gao;Sanqiang Zhao;Jianzhuang Liu

  • Affiliations:

  • School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;Griffith School of Engineering, Griffith University, Brisbane, QLD, Australia;Griffith School of Engineering, Griffith University, Brisbane, QLD, Australia;Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2010

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Abstract

This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n - 1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.