Periocular Biometrics for non-ideal images: with off-the-shelf Deep CNN & Transfer Learning approach

• Published in: Procedia computer science 2020, Vol.167, p.344-352
• Main Authors: Kumari, Punam, Seeja, K.R.
• Format: Article
• Language: English
• Subjects: Convolutional Neural Networks; Deep Learning; Periocular Biometrics; Transfer Learning
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2020.03.234

Iris and face are two most popular biometric traits. Unfortunately, their performance can be degraded when these systems encountered with non-ideal images. The primary motivation of this paper is to analyze the usability of a new feature rich biometric trait known as periocular region (nearby region around the eye) for two different non-ideal scenarios 1) Matching of images with different pose variation and 2) Matching of different side of periocular images (Left eye to Right eye & Right eye to Left eye). In this paper, seven different off-the-shelf deep learning based Convolutional Neural Networks (CNN) using transfer learning approach are implemented on UBIPr database. Experimental results demonstrated that for images with pose variation VGG 19 obtained maximum recognition accuracy while for matching of two different side of periocular region Res Net 18 outperform others. As an additional contribution, a novel approach for the use of mirror images to improve the matching accuracy of two different side of periocular region is also presented.