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Explicit Integration of Identity Information from Skin Regions to Improve Face Recognition

  • Garsah Farhan Al-Qarni
  • Farzin Deravi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

This paper investigates the possibility of exploiting facial skin texture regions to further improve the performance of face recognition systems. Information extracted from the forehead region is combined with scores produced by a kernel-based face recognition algorithm in a novel framework that can adapt to the availability of pure skin patches. A novel skin/non-skin classifier is presented for detecting such pure skin patches in the forehead region using state-of-the-art texture feature extraction techniques. The pure-skin forehead image regions are then classified using a sparse representation classifier to produce scores which are fused with the results of whole-face classifiers. The proposed algorithm is tested using the XM2VTS database and compared with other results published using similar protocols. The results suggest that exploiting pure skin regions in such an adaptive framework could significantly enhance recognition accuracy.

Keywords

Face Recognition Face Image Sparse Representation Skin Region Identity Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Computation 12, 2385–2404 (2000)CrossRefGoogle Scholar
  2. 2.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2002) Software available at, http://www.csie.ntu.edu.tw/~cjlin/libsvm
  3. 3.
    Chen, L.-F., Liao, H.-Y., Ko, M.-T., Lin, J.-C., Yu, G.-J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33, 1713–1726 (2000)CrossRefGoogle Scholar
  4. 4.
    Li, Z., Lin, D., Tang, X.: Nonparametric discriminant analysis for face recognition. IEEE T-PAMI 31(4), 755–761 (2009)CrossRefGoogle Scholar
  5. 5.
    Lin, D., Tang, X.: Recognize High Resolution Faces: From Macrocosm to Microcosm. In: CVPR, pp. 1355–1362 (2006)Google Scholar
  6. 6.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE T- Neural Networks 14, 117–126 (2003)CrossRefGoogle Scholar
  7. 7.
    Kyrki, V., Kamarainen, J.-K., Klviinen, H.: Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters 25, 311–318 (2003)CrossRefGoogle Scholar
  8. 8.
    Ojala, T., Pietikinen, M., Menp, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE T-PAMI 24, 971–987 (2002)CrossRefGoogle Scholar
  9. 9.
    Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1999)CrossRefGoogle Scholar
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511–518 (2001)Google Scholar
  11. 11.
    Wang, X., Tang, X.: Bayesian Face Recognition Using Gabor Features. In: WBMA, pp. 70–73 (2003)Google Scholar
  12. 12.
    Wright, J., Yang, A.Y., Ganesh, A., Shankar Sastry, S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE T-PAMI 31(2), 210–227 (2009)CrossRefGoogle Scholar
  13. 13.
    Zhang, B.-L., Zhang, H., Ge, S.S.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE T- Neural Network 15, 166–177 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Garsah Farhan Al-Qarni
    • 1
  • Farzin Deravi
    • 1
  1. 1.School of Engineering and Digital ArtsUniversity of KentCanterburyUK

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