Attack of Mechanical Replicas: Liveness Detection With Eye Movements

Abstract

This paper investigates liveness detection techniques in the area of eye movement biometrics. We investigate a specific scenario, in which an impostor constructs an artificial replica of the human eye. Two attack scenarios are considered: 1) the impostor does not have access to the biometric templates representing authentic users, and instead utilizes average anatomical values from the relevant literature and 2) the impostor gains access to the complete biometric database, and is able to employ exact anatomical values for each individual. In this paper, liveness detection is performed at the feature and match score levels for several existing forms of eye movement biometric, based on different aspects of the human visual system. The ability of each technique to differentiate between live and artificial recordings is measured by its corresponding false spoof acceptance rate, false live rejection rate, and classification rate. The results suggest that eye movement biometrics are highly resistant to circumvention by artificial recordings when liveness detection is performed at the feature level. Unfortunately, not all techniques provide feature vectors that are suitable for liveness detection at the feature level. At the match score level, the accuracy of liveness detection depends highly on the biometric techniques employed.Index Terms-Biometrics, liveness detection, spoofs, attack vectors, eye movements, pattern analysis, security and protection.

Using Reflexive Eye Movements for Fast Challenge-Response Authentication

“…Firstly, the adversary is likely to be solving a harder problem than the system; while the system needs to build a discriminative model that allows making a binary decision about user's identity, the adversary needs to actually generate eye movements which correspond to the legitimate user. An indication of the difficulty of artificially creating eye-movements can be found in work by Komogortsev et al [28], which evaluated the complexity of a significantly simpler problem: artificially generating 1-dimensional eye movements. The paper showed that those movements could be distinguished from natural recordings with high accuracy; creating realistic 2D eye-movements that correspond to a specific user is likely to be significantly harder.…”

An Intelligent Approach for Anti-Spoofing in a Multimodal Biometric System

“…Oleg el al. [18] proposed Liveness detection techniques in the area of eye movement biometrics. Two attack scenarios were considered, in which the imposter does and does not have direct access to the biometric database.…”

Current research in eye movement biometrics: An analysis based on BioEye 2015 competition

“…For example, soft biometric traits extracted by the eye movements can provide advanced antispoofing resistance [23,24] and facilitate tasks like continuous authentication [25]. Other advantages of eye movement biometrics are the provided ability for touch-less (remote) capturing, and the potential employment of the extracted features for medical monitoring purposes [26,27].…”