Detection of Glasses in Near-Infrared Ocular Images

Eyeglasses change the appearance and visual perception of facial images. Moreover, under objective metrics, glasses generally deteriorate the sample quality of near-infrared ocular images and as a consequence can worsen the biometric performance of iris recognition systems. Automatic detection of glasses is therefore one of the prerequisites for a sufficient quality, interactive sample acquisition process in an automatic iris recognition system. In this paper, three approaches (i.e. a statistical method, a deep learning based method and an algorithmic method based on detection of edges and reflections) for automatic detection of glasses in near-infrared iris images are presented. Those approaches are evaluated using cross-validation on the CASIA-IrisV4-Thousand dataset, which contains 20000 images from 1000 subjects. Individually, they are capable of correctly classifying 95-98% of images, while a majority vote based fusion of the three approaches achieves a correct classification rate (CCR) of 99.54%.acceptedVersion© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works