Privacy Enhancement in Biometric Systems by Template Protection using Deep Learning

Harshita Kukreja¹ , Soumya Jain² , Shefali Arora³ , M.P.S. Bhatia⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.1 , pp.14-24, Feb-2021

Online published on Feb 28, 2021

Copyright © Harshita Kukreja, Soumya Jain, Shefali Arora, M.P.S. Bhatia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Biometric recognition is playing a vital role in various applications these days. Biometric traits like fingerprints, face, and iris are used for the identification of individuals. The matching is done by authenticating against a set of enrolled templates in the biometric system. These templates are vulnerable to attacks and any leakage of information or data can lead to a breach of privacy of individuals. In this paper, we review the existing techniques that have been used to secure biometric templates in a system. We also present a case study in which we propose a framework involving hashing of biometric templates using SHA-256 algorithm, which is followed by extraction of features and classification using deep convolutional neural networks.

Key-Words / Index Term :
Template Protection, Biometrics, Hashing, Deep Learning, Convolutional Neural Networks

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