Gavrilova, Marina L.Sudhakar, Tanuja2020-09-292020-09-292020-09-24Sudhakar, T. (2020). Cancelable Biometric System Based on Deep Learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/112603With the increasing number of cyberattacks, Personal Identification Numbers (PINs), tokens, and passwords have been found to be insufficient for identity protection. Over the past decade, biometric systems have gained high popularity in providing secure mechanisms for user authentication. In this thesis, the safety of biometric data is rendered through the technique of ‘Cancelable Biometrics’. A cancelable biometric system for multi-instance biometrics has been designed with the use of deep learning. A deep learning architecture based on Convolutional Neural Network (CNN) and Multi Layer Perceptron (MLP) is presented to create a novel, accurate, and secure cancelable biometric solution. An implementation of the proposed solution has also been carried out on the cloud platform to provide a ubiquitous cancelable biometric authentication service. A high authentication accuracy, biometric template security and cancelability, fast response times, and cost efficiency are the merits of the presented cancelable biometric system.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.BiometricsCancelable biometricsDeep learningCloudBiometric securityMachine learningEducation--SciencesComputer Sciencemaster thesis10.11575/PRISM/38262