Ternary eye movement classification, which separates fixations, saccades, and smooth pursuit from the raw eye positional data, is extremely challenging. This article develops new and modifies existing eye-tracking algorithms for the purpose of conducting meaningful ternary classification. To this end, a set of qualitative and quantitative behavior scores is introduced to facilitate the assessment of classification performance and to provide means for automated threshold selection. Experimental evaluation of the proposed methods is conducted using eye movement records obtained from 11 subjects at 1000 Hz in response to a step-ramp stimulus eliciting fixations, saccades, and smooth pursuits. Results indicate that a simple hybrid method that incorporates velocity and dispersion thresholding allows producing robust classification performance. It is concluded that behavior scores are able to aid automated threshold selection for the algorithms capable of successful classification.

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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.

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A novel approach that performs liveness detection for biometric modalities that use eye movement signal for person identification is proposed and evaluated. Liveness detection is done via estimation and analysis of the internal non-visible anatomical structure of the human eye termed Oculomotor Plant Characteristics (OPC). At this stage of its development the OPC approach targets prevention of spoof attacks that are generated by the accurate mechanical replicas of the human eye. We generalize and test two classes of such eye replicas via their mathematical representations. Specifically, we investigate following classes of replicas: a) those that are built using default OPC values specified by the research literature, and b) those that are built from the OPC specific to an individual. The results that involved processing live data from 32 individuals over four recording sessions and their eye replicas indicate relatively high theoretical resistance of the OPC liveness detection method to the mechanical attack that impersonates an authentic user.