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Performance evaluation of subspace methods to tackle small sample size problem in face recognition

Published: 03 August 2012 Publication History

Abstract

Linear Discriminant Analysis (LDA) has been one of the popular subspace methods for face recognition. But this method suffers from the small sample size (SSS) problem, also known as 'curse of dimensionality'. Various techniques have been proposed in literature to overcome this limitation. But it is still unclear which method provides the best solution to SSS problem. In this paper, we have investigated the performance of some popular subspace methods such as principal component analysis (PCA), PCA + LDA, LDA via QR decomposition, Null-space LDA, Exponential Discriminant Analysis (EDA), PCA+EDA etc. Extensive experiments have been performed on five publically available face datasets viz. AR, CMU-PIE, PIX, Yale and YaleB. The performance is measured in terms of average classification accuracy. Experimental results show that the performance increases with the increase in the number of images per person in training set irrespective of the datasets. There is no clear winner among the subspace methods under investigation. But, the performance of PCA+LDA and PCA+EDA is consistent in tackling SSS problem irrespective of the dataset and can also handle the illumination variation in face recognition.

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  • (2019)RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognitionMultimedia Tools and Applications10.1007/s11042-019-08228-2Online publication date: 20-Nov-2019
  • (2018)Random permutation principal component analysis for cancelable biometric recognitionApplied Intelligence10.1007/s10489-017-1117-748:9(2824-2836)Online publication date: 1-Sep-2018

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cover image ACM Other conferences
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
August 2012
1307 pages
ISBN:9781450311960
DOI:10.1145/2345396
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 August 2012

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Author Tags

  1. Fisherfaces
  2. LDA via QR decomposition
  3. exponential discriminant analysis
  4. illumination variation
  5. linear discriminant analysis
  6. null-space LDA
  7. small sample size

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  • RPS

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Cited By

View all
  • (2019)RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognitionMultimedia Tools and Applications10.1007/s11042-019-08228-2Online publication date: 20-Nov-2019
  • (2018)Random permutation principal component analysis for cancelable biometric recognitionApplied Intelligence10.1007/s10489-017-1117-748:9(2824-2836)Online publication date: 1-Sep-2018

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