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.
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References
Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Computation 12, 2385–2404 (2000)
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
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)
Li, Z., Lin, D., Tang, X.: Nonparametric discriminant analysis for face recognition. IEEE T-PAMI 31(4), 755–761 (2009)
Lin, D., Tang, X.: Recognize High Resolution Faces: From Macrocosm to Microcosm. In: CVPR, pp. 1355–1362 (2006)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE T- Neural Networks 14, 117–126 (2003)
Kyrki, V., Kamarainen, J.-K., Klviinen, H.: Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters 25, 311–318 (2003)
Ojala, T., Pietikinen, M., Menp, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE T-PAMI 24, 971–987 (2002)
Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1999)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511–518 (2001)
Wang, X., Tang, X.: Bayesian Face Recognition Using Gabor Features. In: WBMA, pp. 70–73 (2003)
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)
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)
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Al-Qarni, G.F., Deravi, F. (2012). Explicit Integration of Identity Information from Skin Regions to Improve Face Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_4
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DOI: https://doi.org/10.1007/978-3-642-31298-4_4
Publisher Name: Springer, Berlin, Heidelberg
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