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

Generating and analyzing synthetic finger vein images

Abstract: The finger-vein biometric offers higher degree of security, personal privacy and strong anti-spoofing capabilities than most other biometric modalities employed today. Emerging privacy concerns with the database acquisition and lack of availability of large scale finger-vein database have posed challenges in exploring this technology for large scale applications. This paper details the first such attempt to synthesize finger-vein images and presents analysis of synthesized images for the biometrics authentication. We generate a database of 50,000 finger vein images, corresponding to 5000 different subjects, with 10 different synthesized finger-vein images from each of the subject. We use tractable probability models to compare synthesized finger-vein images with the real finger- vein images for their image variability. This paper also presents matching accuracy using the synthesized finger-vein database from 5000 different subjects, using 225000 genuine and 1249750000 impostor matching scores, which suggests significant promises from this finger-vein biometric modality for large scale biometrics applications

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

Analysis and synthesis of iris images

Of all the physiological traits of the human body that help in personal identification, the iris is probably the most robust and accurate. Although numerous iris recognition algorithms have been proposed, the underlying processes that define the texture of irises have not been extensively studied. In this thesis, multiple pair-wise pixel interactions have been used to describe the textural content of the iris image thereby resulting in a Markov Random Field (MRF) model for the iris image. This information is expected to be useful for the development of user-specific models for iris images, i.e. the matcher could be tuned to accommodate the characteristics of each user\u27s iris image in order to improve matching performance. We also use MRF modeling to construct synthetic irises based on iris primitive extracted from real iris images. The synthesis procedure is deterministic and avoids the sampling of a probability distribution making it computationally simple. We demonstrate that iris textures in general are significantly different from other irregular textural patterns. Clustering experiments indicate that the synthetic irises generated using the proposed technique are similar in textural content to real iris images

2D and 3D computer vision analysis of gaze, gender and age

Human-Computer Interaction (HCI) has been an active research area for over four decades. Research studies and commercial designs in this area have been largely facilitated by the visual modality which brings diversified functionality and improved usability to HCI interfaces by employing various computer vision techniques. This thesis explores a number of facial cues, such as gender, age and gaze, by performing 2D and 3D based computer vision analysis. The ultimate aim is to create a natural HCI strategy that can fulfil user expectations, augment user satisfaction and enrich user experience by understanding user characteristics and behaviours. To this end, salient features have been extracted and analysed from 2D and 3D face representations; 3D reconstruction algorithms and their compatible real-world imaging systems have been investigated; case study HCI systems have been designed to demonstrate the reliability, robustness, and applicability of the proposed method.More specifically, an unsupervised approach has been proposed to localise eye centres in images and videos accurately and efficiently. This is achieved by utilisation of two types of geometric features and eye models, complemented by an iris radius constraint and a selective oriented gradient filter specifically tailored to this modular scheme. This approach resolves challenges such as interfering facial edges, undesirable illumination conditions, head poses, and the presence of facial accessories and makeup. Tested on 3 publicly available databases (the BioID database, the GI4E database and the extended Yale Face Database b), and a self-collected database, this method outperforms all the methods in comparison and thus proves to be highly accurate and robust. Based on this approach, a gaze gesture recognition algorithm has been designed to increase the interactivity of HCI systems by encoding eye saccades into a communication channel similar to the role of hand gestures. As well as analysing eye/gaze data that represent user behaviours and reveal user intentions, this thesis also investigates the automatic recognition of user demographics such as gender and age. The Fisher Vector encoding algorithm is employed to construct visual vocabularies as salient features for gender and age classification. Algorithm evaluations on three publicly available databases (the FERET database, the LFW database and the FRCVv2 database) demonstrate the superior performance of the proposed method in both laboratory and unconstrained environments. In order to achieve enhanced robustness, a two-source photometric stereo method has been introduced to recover surface normals such that more invariant 3D facia features become available that can further boost classification accuracy and robustness. A 2D+3D imaging system has been designed for construction of a self-collected dataset including 2D and 3D facial data. Experiments show that utilisation of 3D facial features can increase gender classification rate by up to 6% (based on the self-collected dataset), and can increase age classification rate by up to 12% (based on the Photoface database). Finally, two case study HCI systems, a gaze gesture based map browser and a directed advertising billboard, have been designed by adopting all the proposed algorithms as well as the fully compatible imaging system. Benefits from the proposed algorithms naturally ensure that the case study systems can possess high robustness to head pose variation and illumination variation; and can achieve excellent real-time performance. Overall, the proposed HCI strategy enabled by reliably recognised facial cues can serve to spawn a wide array of innovative systems and to bring HCI to a more natural and intelligent state