Biometric Liveness Detection Using Gaze Information

This thesis is concerned with liveness detection for biometric systems and in particular for face recognition systems. Biometric systems are well studied and have the potential to provide satisfactory solutions for a variety of applications. However, presentation attacks (spoofng), where an attempt is made at subverting them system by making a deliberate presentation at the sensor is a serious challenge to their use in unattended applications. Liveness detection techniques can help with protecting biometric systems from attacks made through the presentation of artefacts and recordings at the sensor. In this work novel techniques for liveness detection are presented using gaze information. The notion of natural gaze stability is introduced and used to develop a number of novel features that rely on directing the gaze of the user and establishing its behaviour. These features are then used to develop systems for detecting spoofng attempts. The attack scenarios considered in this work include the use of hand held photos and photo masks as well as video reply to subvert the system. The proposed features and systems based on them were evaluated extensively using data captured from genuine and fake attempts. The results of the evaluations indicate that gaze-based features can be used to discriminate between genuine and imposter. Combining features through feature selection and score fusion substantially improved the performance of the proposed features

Gaze Stability for Liveness Detection

Spoofing attacks on biometric systems are one of the major impediments to their use for secure unattended applications. This paper explores features for face liveness detection based on tracking the gaze of the user. In the proposed approach, a visual stimulus is placed on the display screen, at apparently random locations, which the user is required to follow while their gaze is measured. This visual stimulus appears in such a way that it repeatedly directs the gaze of the user to specific positions on the screen. Features extracted from sets of collinear and colocated points are used to estimate the liveness of the user. Data is collected from genuine users tracking the stimulus with natural head/eye movements and impostors holding a photograph, looking through a 2D mask or replaying the video of a genuine user. The choice of stimulus and features are based on the assumption that natural head/eye coordination for directing gaze results in a greater accuracy and thus can be used to effectively differentiate between genuine and spoofing attempts. Tests are performed to assess the effectiveness of the system with these features in isolation as well as in combination with each other using score fusion techniques. The results from the experiments indicate the effectiveness of the proposed gaze-based features in detecting such presentation attacks

Biometric Presentation Attack Detection for Mobile Devices Using Gaze Information

Facial recognition systems are among the most widely deployed in biometric applications. However, such systems are vulnerable to presentation attacks (spoofing), where a person tries to disguise as someone else by mimicking their biometric data and thereby gaining access to the system. Significant research attention has been directed toward developing robust strategies for detecting such attacks and thus assuring the security of these systems in real-world applications. This thesis is focused on presentation attack detection for face recognition systems using a gaze tracking approach. The proposed challenge-response presentation attack detection system assesses the gaze of the user in response to a randomly moving stimulus on the screen. The user is required to track the moving stimulus with their gaze with natural head/eye movements. If the response is adequately similar to the challenge, the access attempt is seen as genuine. The attack scenarios considered in this work included the use of hand held displayed photos, 2D masks, and 3D masks. Due to the nature of the proposed challenge-response approaches for presentation attack detection, none of the existing public databases were appropriate and a new database has been collected. The Kent Gaze Dynamics Database (KGDD) consists of 2,400 sets of genuine and attack-based presentation attempts collected from 80 participants. The use of a mobile device were simulated on a desktop PC for two possible geometries corresponding to mobile phone and tablet devices. Three different types of challenge trajectories were used in this data collection exercise. A number of novel gaze-based features were explored to develop the presentation attack detection algorithm. Initial experiments using the KGDD provided an encouraging indication of the potential of the proposed system for attack detection. In order to explore the feasibility of the scheme on a real hand held device, another database, the Mobile KGDD (MKGDD), was collected from 30 participants using a single mobile device (Google Nexus 6), to test the proposed features. Comprehensive experimental analysis has been performed on the two collected databases for each of the proposed features. Performance evaluation results indicate that the proposed gaze-based features are effective in discriminating between genuine and presentation attack attempts

Gaze-based Presentation Attack Detection for Users Wearing Tinted Glasses

Biometric authentication is vulnerable to presentation (spoofing) attacks. It is important to address the security vulnerability of spoofing attacks where an attacker uses an artefact presented at the sensor to subvert the system. Gaze-tracking has been proposed for such attack detection. In this paper, we explore the sensitivity of a gaze-based approach to spoofing detection in the presence of eye-glasses that may impact detection performance. In particular, we investigate the use of partially tinted glasses such as may be used in hazardous environments or outdoors in mobile application scenarios The attack scenarios considered in this work include the use of projected photos, 2D and 3D masks. A gaze-based spoofing detection system has been extensively evaluated using data captured from volunteers performing genuine attempts (with and without wearing such tinted glasses) as well as spoofing attempts using various artefacts. The results of the evaluations indicate that the presence of tinted glasses has a small impact on the accuracy of attack detection, thereby making the use of such gaze-based features possible for a wider range of applications

Directed Gaze Trajectories for Biometric Presentation Attack Detection

Presentation attack artefacts can be used to subvert the operation of biometric systems by being presented to the sensors of such systems. In this work, we propose the use of visual stimuli with randomised trajectories to stimulate eye movements for the detection of such spoofing attacks. The presentation of a moving visual challenge is used to ensure that some pupillary motion is stimulated and then captured with a camera. Various types of challenge trajectories are explored on different planar geometries representing prospective devices where the challenge could be presented to users. To evaluate the system, photo, 2D mask and 3D mask attack artefacts were used and pupillary movement data were captured from 80 volunteers performing genuine and spoofing attempts. The results support the potential of the proposed features for the detection of biometric presentation attacks

Biometric antispoofing methods: A survey in face recognition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Galbally, S. Marcel and J. Fierrez, "Biometric Antispoofing Methods", IEEE Access, vol.2, pp. 1530-1552, Dec. 2014In recent decades, we have witnessed the evolution of biometric technology from the rst pioneering works in face and voice recognition to the current state of development wherein a wide spectrum of highly accurate systems may be found, ranging from largely deployed modalities, such as ngerprint, face, or iris, to more marginal ones, such as signature or hand. This path of technological evolution has naturally led to a critical issue that has only started to be addressed recently: the resistance of this rapidly emerging technology to external attacks and, in particular, to spoo ng. Spoo ng, referred to by the term presentation attack in current standards, is a purely biometric vulnerability that is not shared with other IT security solutions. It refers to the ability to fool a biometric system into recognizing an illegitimate user as a genuine one by means of presenting a synthetic forged version of the original biometric trait to the sensor. The entire biometric community, including researchers, developers, standardizing bodies, and vendors, has thrown itself into the challenging task of proposing and developing ef cient protection methods against this threat. The goal of this paper is to provide a comprehensive overview on the work that has been carried out over the last decade in the emerging eld of antispoo ng, with special attention to the mature and largely deployed face modality. The work covers theories, methodologies, state-of-the-art techniques, and evaluation databases and also aims at providing an outlook into the future of this very active eld of research.This work was supported in part by the CAM under Project S2009/TIC-1485, in part by the Ministry of Economy and Competitiveness through the Bio-Shield Project under Grant TEC2012-34881, in part by the TABULA RASA Project under Grant FP7-ICT-257289, in part by the BEAT Project under Grant FP7-SEC-284989 through the European Union, and in part by the Cátedra Universidad Autónoma de Madrid-Telefónica