Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
Bayesian face recognition using Gabor features
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Recognize High Resolution Faces: From Macrocosm to Microcosm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Discriminant Analysis for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
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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.