Abstract
This paper presents a novel approach for face recognition (FR) based on a new multiscale directional approach, called Shear let Network (SN), and on a recently emerged machine learning paradigm, called Multi-Task Sparse Learning (MTSL). SN aims to extract anisotropic features from an image in order to efficiently capture the facial geometry (shear face), MTSL is used to exploit the relationships among multiple shared tasks generated by changing the regularization parameter to make the optimization convex. We compare our algorithm, called Sparse Multi-Regularized Shear let Network (SMRSN), against different state-of-the-art methods on different experimental protocols with AR, ORL, LFW, FERET, FRGC v1 and Lab2 databases. Our tests show that the SMRSN approach yields a very competitive performance and outperforms several standard methods of FR.