Sparse multi-stage regularized feature learning for robust face recognition

Abstract

Abstract-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.

Efficient Face Recognition Using Regularized Adaptive Non-Local Sparse Coding

“…Dong et al [17] proposed the centralized sparse representation (CSR) for the classification then applied the Bayesian interpretation of the CSR to provide the adaptive regularization parameters. In [30] addressed the regularization parameters to improve the classifier accuracy. The authors proposed Multi-Task Sparse Learning (MTSL) framework based on multiple shared stages.…”

Recognizing faces prone to occlusions and common variations using optimal face subgraphs

“…As an interest, it is a challenging task where the performance of a typical face recognition algorithm usually degrades considerably due to several variations such as expressions, illumination conditions and facial occlusions [32,15]. Therefore, developing a robust face recognition system amidst these variations is still an open research area as stated in recent studies [45,12,1,17]. Face recognitions systems have an essential role in biometric-oriented video surveillance systems which have been progressively incorporated in operational environments where the problem of encountering occlusions cannot be avoided.…”