In this paper, we investigate face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, three state of the art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are challenged against the IRIS-M3 multispectral face data base to evaluate their robustness against high illumination variation. Second, we propose to enhance the Performance of the three mentioned algorithms, which has been drastically decreased because of the non-monotonic illumination variation that distinguishes the IRIS-M3 face database. Instead of the usual braod band images, we use narrow band sub spectral images selected from the visible spectrum. Selection of best spectral bands is formulated 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. The results highlight further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our sub spectral images based approach to increase the accuracy of the studied algorithms by at least 14% upon the proposed database.