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Performance evaluation of linear subspace methods for face recognition under illumination variation

Published: 16 May 2011 Publication History

Abstract

Due to high dimensionality of face images and finite number of training samples, the linear subspace technique for face recognition pose challenges for better performance. In this paper, we compare the performance of linear subspace methods which involve the computation of scatter matrices for face recognition under illumination variation. The performance of these methods is evaluated in terms of classification accuracy, computational training and testing time. Extensive empirical experiments are performed to compare the performance using AR, Pie, Yale and YaleB face databases. In absence of sufficient number of training samples, classification accuracy of linear subspace methods deteriorate. Experimental results show that the performance of Dual LDA is best in terms of average classification accuracy. It is also observed that Fisherface takes minimum training time and both ERE and SVM takes minimum testing time. No linear subspace method outperforms others in terms of all performance measures.

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cover image ACM Conferences
C3S2E '11: Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
May 2011
162 pages
ISBN:9781450306263
DOI:10.1145/1992896
  • General Chair:
  • Bipin C. Desai,
  • Program Chairs:
  • Alain Abran,
  • Sudhir P. Mudur
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Published: 16 May 2011

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Author Tags

  1. face recognition
  2. illumination variation
  3. linear subspace
  4. scatter matrices

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C3S2E '11
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  • ACM
  • Concordia University

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