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

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

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

Restrictive Voting Technique for Faces Spoofing Attack

Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications

RGBD Datasets: Past, Present and Future

Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style