Self-organized Gabor features for pose invariant face recognition

Saleh Aly, Naoyuki Tsuruta, Rin Ichiro Taniguchi

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

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Number of pages10
EditionPART 1
Publication statusPublished - 2009
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: Dec 1 2009Dec 5 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5863 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th International Conference on Neural Information Processing, ICONIP 2009

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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