Advertisement

Pattern Analysis and Applications

, Volume 13, Issue 1, pp 79–83 | Cite as

Face recognition with adaptive local hyperplane algorithm

  • Tao Yang
  • Vojislav Kecman
Theoretical Advances

Abstract

The paper introduces a novel adaptive local hyperplane (ALH) classifier and it shows its superior performance in the face recognition tasks. Four different feature extraction methods (2DPCA, (2D)2PCA, 2DLDA and (2D)2LDA) have been used in combination with five classifiers (K-nearest neighbor (KNN), support vector machine (SVM), nearest feature line (NFL), nearest neighbor line (NNL) and ALH). All the classifiers and feature extraction methods have been applied to the renown benchmarking face databases—the Cambridge ORL database and the Yale database and the ALH classifier with a LDA based extractor outperforms all the other methods on them. The ALH algorithm on these two databases is very promising but more study on larger databases need yet to be done to show all the advantages of the proposed algorithm.

Keywords

Face recognition Adaptive local hyperplane ALH 

References

  1. 1.
    Kecman V (2001) Learning and soft computing, support vector machines, neural networks and fuzzy logic models. The MIT Press, CambridgeMATHGoogle Scholar
  2. 2.
    Li SZ, Lu J (1999) Face recognition using the nearest feature line method. IEEE Trans Neural Netw 10:439–443CrossRefGoogle Scholar
  3. 3.
    Zheng W, Zhao L, Zou C (2004) Locally nearest neighbor classifiers for pattern classification. Pattern Recognit 37:1307–1309MATHCrossRefGoogle Scholar
  4. 4.
    Yang T, Kecman V (2008) Adaptive local hyerplane classification. J Neurocomput 71:3001–3004CrossRefGoogle Scholar
  5. 5.
    Vincent P, Bengio Y (2002) K-local hyperplane and convex distance nearest neighbor algorithms. In: Advances in neural information processing systems (NIPS), vol 14. MIT Press, Cambridge, pp 985–992Google Scholar
  6. 6.
    Li SZ (1998) Face recognition based on nearest linear combinations. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. Santa Barbara, pp 839–844Google Scholar
  7. 7.
    Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26:131–137CrossRefGoogle Scholar
  8. 8.
    Ming L, Yuan B (2005) 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26:527–532CrossRefGoogle Scholar
  9. 9.
    Zhang D, Zhou ZH (2005) (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. J Neurocomput 69:224–231CrossRefGoogle Scholar
  10. 10.
    Noushatha S, Kumara GH, Shivakumarab P (2006) (2D)2LDA: An efficient approach for face recognition. Pattern Recognit 39:1396–1400CrossRefGoogle Scholar
  11. 11.
    Chien JT, Wu CC (2002) Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition. IEEE Trans Pattern Anal Mach Intell 24:1644–1649CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Faculty of EngineeringThe University of AucklandAucklandNew Zealand
  2. 2.Virginia Commonwealth UniversityRichmondUSA

Personalised recommendations