A kernelized sparsity-based approach for best spectral bands selection for face recognition

We study face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, the robustness of four state-of-the-art algorithms, namely Multi-block Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP), Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and Patterns of Oriented Edge Magnitudes (POEM-WPCA) against high illumination variation is studied. Second, we propose to enhance the performance of the four mentioned algorithms, which has been drastically decreased upon the day lighted face images provided by IRIS-M³ face database. For this purpose, we use visible narrow band subspectral images selected from the mentioned database. We formulate best spectral bands selection as a pursuit optimization problem wherein the vector of weights determining the importance of each visible spectral band is supposed to be sparse, and hence can be determined by minimizing its L1-norm. Several fusing approaches are then applied on selected best spectral bands using multi-scale and multi-orientation Gabor wavelets. The results highlight further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach with its two components; bands selection and bands fusion, to increase the accuracy of the studied algorithms by at least 14 % upon the proposed database.