Face recognition systems based on ’conventional’ images have reached a significant level of maturity with some practical successes. However, their performance may degrade under poor and/or changing illumination. Multispectral imagery represents a viable alternative to conventional imaging in the search for a robust and practical identification system. Multi- spectral imaging (MI) can be defined as a ’collection of several monochrome images of the same scene, each of them taken with additional receptors sensitive to other frequencies of the visible light or to frequencies beyond the visible light like the infrared region of electro- magnetic continuum. Each image is referred to as a band or a channel. However, one weakness of MI is that they may significantly increase the system processing time because of the huge quantity of data to be mined; in some cases, hundreds of MI are taken for each subject. In this thesis, we propose to solve this problem by developing new approaches to select the set of best visible spectral bands for face matching. For this purpose, the problem of best spectral bands selection is formulated as an optimization problem where spectral bands are constrained to maximize the recognition accuracy under challenging imaging conditions. We reduce the redundancy of both spectral and spatial information without losing valuable details needed for the object recognition, discrimination and classification. We have investigated several mathematic and optimization tools widely used in the field of image processing. One of the approaches we have proposed formulated the problem of best spectral bands selection as a pursuit problem where weights of importance were affected to each spectral band and the vector of all weights was constrained to be sparse with most of its elements are zeros. In another work, we have assigned to each spectral band a linear discriminant analysis (LDA) based weak classifier. Then, all weak classifiers were boosted together using an Adaboost process. From this later, each weak classifier obtained a weight that characterizes its importance and hence the quality of the corresponding spectral band. Several other techniques were also used for best spectral bands selection including but not limited to mixture of Gaussian based modeling, multilinear sparse decomposition, image quality factors, local descriptors like SURF and HGPP, likelihood ratio and so on. These different techniques enabled to build systems for best spectral bands selection that are either static with the same bands are selected for all the subjects or dynamic with each new subject get its own set of best bands. This latter category, dynamic systems, is an original component of our work that, to the best of our knowledge, has not been proposed before; all existing systems are only static. Finally, the proposed algorithms were compared to state-of-the-art algorithms developed for face recognition purposes in general and specifically for best spectral bands selection.