Biometric Face Recognition System using SURF Based Approach

Abstract

Face recognition can be viewed as the problem of robustly identifying an image of a human face, given some database of known faces [6]. We propose a novel, SURF based approach to the problem of face recognition. Although the results are not gratifying our proposed approach loosens the burden of creating the sub spaces as is done in PCA, LDA and the most recent Bayesian approach. Also, during the experiments even though we used an unturned program for the proposed approach, it outperforms the basic PCA and LDA based approaches in terms of consistency.

This article presents a scale-invariant and novel rotation detector and descriptor known as SURF (Speeded-Up Robust Features). SURF outperforms previously defined schemes with respect to repeatability as well as distinctiveness and robustness. It’s computing and comparing can be much faster. This is done by relying on integral images for image convolutions; by making the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. Its result is a combination of novel detection, description, and finding match steps. The paper contains an overview of the detector and descriptor and then finds out the effects of the most important parameters. The article is concluded with SURF’s application to two challenging. Yet it converse goals i.e. camera calibration which is a special case of image registration and recognition of objects. Our experiments show that SURF is very useful in vast areas of computer vision.