A Novel Framework For Face Recognition Using Robust Local Representation-Based Classification

Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems.