A Sparse and Inlier Based SIFT and SURF Features for Automated Face Recognition

Face Recognition (FR) is a prolific form of biometric that has spawned a myriad of inventive applications for commercial and law-enforcement scenarios and has fostered several novel research directions. In the FR process, the choice of the feature extractor governs the overall efficiency and in that regard, SIFT and SURF are two prominent feature extraction mechanisms that are frequently employed due to their robustness with respect to scale, translation, illumination and rotation. Even though the SIFT and SURF descriptors are immensely effective, they are cluttered with redundant key-points and noise, which we aim to tackle by employing the Sparse Singular Value Decomposition (SSVD) method to perform Dimensionality Reduction, and RANSAC to remove noise in the form of outliers. In this paper, we will conclusively demonstrate by utilizing extensive mathematical arguments, and by performing exhaustive experimentations over the benchmark ORL and LFPW databases, that the proposed SIFT-SSVD-RANSAC and SURF-SSVD-RANSAC methodologies are more effective than their classical counterparts, as they are capable of handling extreme variations between the matched images in terms of scale, zoom, view-point and so on. The findings proffered by this work, coupled with our other studies, form a series intended to aid developers in making prudent decisions in order to build proficient FR systems.