Feature Extractionusing ORB-RANSAC for Face Recognition

Abstract

Abstract Face Recognition is one of the most thriving and cutting- edge fields of research that stands unwaveringly as the most critically challenging problems in the domain of Computer Vision. In the design of an efficient FR system, the potency of the feature descriptor dictates the proficiency of the recognition performance. The effective employment of FR systems in handheld and mobile devices is hindered by their lack of GPU acceleration, which considerably limits their computational capabilities and furthermore, the prevalent SURF and SIFT feature descriptors are computationally expensive and hence are not viable for low-powered devices. To that end, ORB, a cost effective feature descriptor, has been effective for such devices. Hence, in this paper, we propose a novel technique that utilizes ORB, and in turn, address a few of its crucial inadequacies by incorporating RANSAC as a post-processing step to remove redundant key-points and noise (in the form of outliers) and hence improve ORB's efficacy to proffer a robust system for facial image recognition, that has improved accuracy than the prevalent state-of-the-art methods such as SIFT-RANSAC and SURF-RANSAC.