Face recognition with learned local curvelet patterns and 2-directional L1-norm based 2DPCA

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose Learned Local Curvelet Patterns (LLCP) for presenting the local features of facial images. The proposed method is based on curvelet transform which can overcome the weakness of traditional Gabor wavelets in higher dimension, and better capture the curve singularities and hyperplane singularities of facial images. Different from wavelet transform, curvelet transform can effectively and efficiently approximate the curved edges with very few coefficients as well as taking space-frequency information into consideration. First, LLCP designs several learned codebooks from Curvelet filtered facial images. Then each facial image can be encoded into multiple pattern maps and finally block-based histograms of these patterns are concatenated into an histogram sequence to be used as a face descriptor. In order to reduce the face feature descriptor, 2-Directional L1-Norm Based 2DPCA ((2D)2PCA-L1) is proposed which is simultaneously considering the row and column directions for efficient face representation and recognition. Performance assessment in several face recognition problem shows that the proposed approach is superior to traditional ones.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2012 International Workshops, Revised Selected Papers
Pages109-120
Number of pages12
EditionPART 1
DOIs
Publication statusPublished - 2013 Apr 15
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 2012 Nov 52012 Nov 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7728 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Asian Conference on Computer Vision, ACCV 2012
CountryKorea, Republic of
CityDaejeon
Period12/11/512/11/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhou, W., & Kamata, S. I. (2013). Face recognition with learned local curvelet patterns and 2-directional L1-norm based 2DPCA. In Computer Vision - ACCV 2012 International Workshops, Revised Selected Papers (PART 1 ed., pp. 109-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7728 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-37410-4_10