A comparison of histogram and template matching for face verification

Abstract

Face identification and verification are parts of a face recognition process. The verification of faces involves the comparison of an input face to a known face to verify the claim of identity of an individual. Hence, the verification process must determine the similarity between two face images, a face object image and a target face image. In order to determine the similarity of faces, different techniques can be used, such as methods based on templates and histograms. In real-world applications, captured face images may suffer variations due to disturbing factors such as image noise, changes in illumination, scaling, rotation and translation. Because of these variations, face verification becomes a complex process. In this context, a comparison between histogram and template matching methods is done in this work using images with variations. Different experiments were conducted to analyze the behavior of these methods and to define which method performs better in artificially generated images.