On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks. In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems. As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks

Design and Performance Evaluation of a Biometric Iris Verification System

This paper describes an iris verification project focused on design and performance evaluation under both matched and mismatched training and testing conditions. Training is always performed on clean iris images. Testing is performed on both clean and noisy iris images. This project is part of a senior undergraduate course on biometric systems. In implementing an iris recognition system, students go through each step, namely, preprocessing, feature extraction, classification (training and use in rendering a decision) and performance evaluation. The Chinese Academy of Sciences - Institute of Automation(CASIA) eye image database known as the CASIA-Iris-Interval-v3 database is used to show students that robustness to mismatched training and testing conditions is a significant practical issue

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, 117, 10, (2013) DOI: 10.1016/j.cviu.2013.06.003A binary iriscode is a very compact representation of an iris image. For a long time it was assumed that the iriscode did not contain enough information to allow for the reconstruction of the original iris. The present work proposes a novel probabilistic approach based on genetic algorithms to reconstruct iris images from binary templates and analyzes the similarity between the reconstructed synthetic iris image and the original one. The performance of the reconstruction technique is assessed by empirically estimating the probability of successfully matching the synthesized iris image against its true counterpart using a commercial matcher. The experimental results indicate that the reconstructed images look reasonably realistic. While a human expert may not be easily deceived by them, they can successfully deceive a commercial matcher. Furthermore, since the proposed methodology is able to synthesize multiple iris images from a single iriscode, it has other potential applications including privacy enhancement of iris-based systems.This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Challenge (TEC2009-11186) and Bio-Shield (TEC2012-34881) from Spanish MECD, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica

State of the Art in Biometric Key Binding and Key Generation Schemes

Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems

A Biometric Approach to Prevent False Use of IDs

What is your username? What is your password? What is your PIN number? These are some of the commonly used key questions users need to answer accurately in order to verify their identity and gain access to systems and their own data. Passwords, Personal Identification Numbers (PINs) and ID cards are different means of tokens used to identify a person, but these can be forgotten, stolen or lost. Currently, University of Hertfordshire (UH) carries out identity checks by checking the photograph on an ID card during exams. Other processes such as attendance monitoring and door access control require tapping the ID card on a reader. These methods can cause issues such as unauthorised use of ID card on attendance system and door access system if ID card is found, lost or borrowed. During exams, this could lead to interruptions when carrying out manual checks. As the invigilator carries out checks whilst the student is writing an exam, it is often difficult to see the student’s face as they face down whilst writing the exam. They cannot be disturbed for the ID check process. Students are also required to sign a manual register as they walk into the exam room. This process is time consuming. A more robust approach to identification of individuals that can avoid the above mentioned limitations of the traditional means, is the use of biometrics. Fingerprint was the first biometric modality that has been used. In comparison to other biometric modalities such as signature and face recognition, fingerprint is highly unique, accepted and leads to a more accurate matching result. Considering these properties of fingerprint biometrics, it has been explored in the research study presented in this thesis to enhance the efficiency and the reliability of the University’s exam process. This thesis focuses on using fingerprint recognition technology in a novel approach to check identity for exams in a University environment. Identifying a user using fingerprints is not the only aim of this project. Convenience and user experience play vital roles in this project whilst improving speed and processes at UH

Mixing Biometric Data For Generating Joint Identities and Preserving Privacy

Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems