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)


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.


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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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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