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

Wei Zhou*, Sei Ichiro Kamata

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルComputer Vision - ACCV 2012 International Workshops, Revised Selected Papers
ページ109-120
ページ数12
PART 1
DOI
出版ステータスPublished - 2013 4 15
イベント11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
継続期間: 2012 11 52012 11 9

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
7728 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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