Periocular Biometrics using Handcrafted Features for Non-Ideal Images

Abstract

Features from face and iris to authenticate individuals are the most popular biometric traits. Still inclusion of non-ideal images (such as images with variation in pose, tilting head, subjects wearing spectacles and variation in capturing device distance) can degrade the recognition accuracy of any biometric systems. For this scenario, periocular region (nearby region around the eye) based biometric authentication is an emerging method which is used by researchers now a days to improve the recognition accuracy specifically for non-ideal images and when users are non- cooperative. In this context, our key insight is to develop a system considering periocular region as a biometric trait and aim to evaluate its effectiveness for classification of non-ideal images in two different non-ideal scenarios 1) images with different pose variation and 2) images captured from varying camera standoff distance. In this proposed work we have evaluated three different handcrafted feature descriptors 1) Histogram of Oriented Gradients 2) Bag of Feature model and 3)Local Binary Patterns on two different databases 1) ORL face database and 2) UBIPr periocular image database and found that HOG feature descriptor show superior performance as compare to BOF and LBP feature descriptor for periocular region based biometric authentication systems.