This paper presents an objective evaluation of various eye movement-based biometric features and their ability to accurately and precisely distinguish unique individuals. Eye movements are uniquely counterfeit resistant due to the complex neurological interactions and the extraocular muscle properties involved in their generation. Considered biometric candidates cover a number of basic eye move ments and their aggregated scan path characteristics, in cluding: fixation count, average fixation duration, average saccade amplitudes, average saccade velocities, average saccade peak velocities, the velocity waveform, scanpath length, scanpath area, regions of interest, scan path inflec tions, the amplitude-duration relationship, the main se quence relationship, and the pairwise distance between fIXations. As well, an information fusion method for com bining these metrics into a single identification algorithm is presented. With limited testing this method was able to identifY subjects with an equal error rate of 27%. These results indicate that scan path-based biometric identifica tion holds promise as a behavioral biometric technique.

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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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