In this paper we present a new multi-view face recognition approach. Besides the recognition performance gain and the computation time reduction, our main objective is to deal with the variability of the face pose (multi-view) in the same class (identity). Several new methods were applied on face images to calculate our biometric templates. The Laplacian Smoothing Transform (LST) and Discriminant Analysis via Support Vectors (SVDA) have been used for the feature extraction and selection. For the classification, we have developed a new inter-communication technique using a model for the automatic pose estimation of the head in a face image. Experimental results conducted on UMIST database show that an average improvement for face recognition performance has been obtained in comparison with several multi-view face recognition techniques in the literature. Moreover, the system maintains a very acceptable running time and a high performance even in uncontrolled conditions.