Self-organized Gabor features for pose invariant face recognition

Saleh Aly, Naoyuki Tsuruta, Rin Ichiro Taniguchi

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

2 被引用数 (Scopus)

抄録

Pose-invariant face recognition using single frontal training image is considered one of the most difficult challenges in face recognition. To address this problem, we introduce a novel feature extraction method based on learning the manifold of local features. Changes in local features due to pose variations induce a nonlinear manifold in the feature space. Self-organizing map is employed to learn the manifold induced by Gabor filter response of a generic training face database captured at various pose directions. Furthermore, this manifold can be used to represent new face image as a set of points in the feature space. A modular Hausdorff distance measure, which can effectively measure the similarity between two point sets without any correspondence, is also proposed to identify unlabeled subjects. Experimental results on CMU-PIE face database show the effectiveness of the novel method against pose variations.

本文言語英語
ホスト出版物のタイトルNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
ページ733-742
ページ数10
PART 1
DOI
出版ステータス出版済み - 12月 1 2009
イベント16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, タイ
継続期間: 12月 1 200912月 5 2009

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
5863 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他16th International Conference on Neural Information Processing, ICONIP 2009
国/地域タイ
CityBangkok
Period12/1/0912/5/09

!!!All Science Journal Classification (ASJC) codes

  • 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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