In this thesis, we describe the problem of automatic face recognition in visibleand long–wave infrared lights. The state of the art methods are described, and westudy, in a first time, a method based on convolutional neural networks. Applied toboth modalities, the proposed fusion method, based on a weighted sum of scores,yields a substantial increasing of the recognition rates. In a second time, ... [Show full abstract] a pretrainingof the network with sparse methods is studied for automatic facial recognition.Finally, we propose an approach based on a sparse decomposition of faces, coupledwith a classification scheme involving in a l1 minimization. This last approach givesgood identification rates on the well known Notre–Dame database.