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IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines applied to face recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Dependent Representations for Illumination Insensitive Image Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted Sub-Gabor for face recognition
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Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of classifiers for illumination robust face recognition
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An image preprocessing algorithm for illumination invariant face recognition
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Enhanced local texture feature sets for face recognition under difficult lighting conditions
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Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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Face recognition under changing lighting conditions is a challenging problem in computer vision. In this paper, we analyze the relative strengths of different lighting insensitive representations, and propose efficient classifier combination schemes that result in better recognition rates. We consider two experimental settings, wherein we study the performance of different algorithms with (and without) prior information on the different illumination conditions present in the scene. In both settings, we focus on the problem of having just one exemplar per person in the gallery. Based on these observations, we design algorithms for integrating the individual classifiers to capture the significant aspects of each representation. We then illustrate the performance improvement obtained through our classifier combination algorithms on the illumination subset of the PIE dataset, and on the extended Yale-B dataset. Throughout, we consider galleries with both homogenous and heterogeneous lighting conditions.