Sparse multi-stage regularized feature learning for robust face recognition

• A new feature-extraction approach for efficient FR based on directional features.
• An augmented multi-stage or multi-regularization convex optimization.
• Results on various face databases demonstrate the effectiveness of our approach.
• The approach presents a trade-off between recognition and computational complexity.
• Three protocol experimental are tested among them the SSPP.

The major limitation in current facial recognition systems is that they do not perform very well in uncontrolled environments, that is, when faces present variations in pose, illumination, facial expressions and environment. This is a serious obstacle in applications such as law enforcement and surveillance systems. To address this limitation, in this paper we introduce an improved approach to ensure robust face recognition, that relies on two innovative ideas. First, we apply a new multiscale directional framework, called Shearlet Network (SN), to extract facial features. The advantage of this approach comes from the highly sparse representation properties of the shearlet framework that is especially designed to robustly extract the fundamental geometric content of an image. Second, we apply a refinement of the Multi-Task Sparse Learning (MTSL) framework to exploit the relationships among multiple shared tasks generated by changing the regularization parameter during the recognition stage. We provide extensive numerical tests to show that our Sparse Multi-Regularized Shearlet Network (SMRSN) algorithm performs very competitively when compared against different state-of-the-art methods on different experimental protocols, including face recognition in uncontrolled conditions and single-sample-per-person.