Complex eye movement pattern biometrics: Analyzing fixations and saccades

Holland, Corey D.; Komogortsev, Oleg V.

doi:10.1109/icb.2013.6612953

Cited by 63 publications

(45 citation statements)

References 16 publications

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“…In [10] is reported a study about the modification of the eyes movements when the environment and the set of stimuli affecting the recorded person are changed. The performance of the system is tested presenting a set different stimulus types from simpler patterns to most complex ones.…”

Section: Related Workmentioning

confidence: 99%

Person identification through entropy oriented mean shift clustering of human gaze patterns

Vella

Infantino

Scardino

2016

Multimed Tools Appl

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The paper describes a system aimed at improving the human machine interaction that is able to identify users according how she looks at the monitor. The proposed system does not need invasive measurements that could limit the naturalness of her actions. The approach, here described, detects the gaze movements on the monitor and clusters the sequences of user gaze fixation points on the screen characterizing the user according the particular patterns her gaze follows. The recognition of the user is performed through a clustering process employing the Mean-Shift algorithm and it can open new perspective in human-machine interaction. In particular, the parameters of the clustering process are tuned optimizing an entropy oriented cost function that allows an automatic selection of the best parameters setting.

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Section: Related Workmentioning

confidence: 99%

Person identification through entropy oriented mean shift clustering of human gaze patterns

Vella

Infantino

Scardino

2016

Multimed Tools Appl

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“…Since it is the core of our concept to have continuous contact with the user's eye during engagements, it is possible to take advantage of the eye features that makes each and every person unique. Iris pattern recognition has been known for decades to be a very reliable biometric method [Burge and Bowyer 2013] and recent research [Holland and Komogortsev 2013] includes motion features as well. It is still an open research question, though, how to best get high-resolution images of the iris and eye when the user is holding the camera on the wrist.…”

Section: Discussionmentioning

confidence: 99%

Wrist-worn pervasive gaze interaction

Hansen

Lund

Biermann

et al. 2016

Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research &Amp; Applications

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This paper addresses gaze interaction for smart home control, conducted from a wrist-worn unit. First we asked ten people to enact the gaze movements they would propose for e.g. opening a door or adjusting the room temperature. On basis of their suggestions we built and tested different versions of a prototype applying off-screen stroke input. Command prompts were given to twenty participants by text or arrow displays. The success rate achieved by the end of their first encounter with the system was 46% in average; it took them 1.28 seconds to connect with the system and 1.29 seconds to make a correct selection. Their subjective evaluations were positive with regard to the speed of the interaction. We conclude that gaze gesture input seems feasible for fast and brief remote control of smart home technology provided that robustness of tracking is improved.

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“…The method that we developed for the detection of the spoof indicative distortions arising during a print-attack, was inspired by the Complex Eye Movement (CEM) Biometrics framework, presented in [19]. In the current approach, instead of identifying fixations and saccades in an eye movement recording, we perform a finer decomposition of the eye movement signal into a set of elementary units consisted of eye micro-movements.…”

Section: Liveness Detection Featuresmentioning

confidence: 99%

“…In the original implementation of the CEM framework [19], several different primitive features were proposed for the description of fixation and saccade basic properties. From the provided ensemble of characteristics we selected the Horizontal Fixation Centroid and the Vertical Fixation Centroid features.…”

Section: Liveness Detection Featuresmentioning

confidence: 99%

Gaze estimation as a framework for iris liveness detection

Rigas

Komogortsev

2014

IEEE International Joint Conference on Biometrics

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This work investigates the possibility of detecting iris print-attacks via the analysis of a number of gaze-related features acquired in a process of eye tracking. Gaze estimation algorithms employ models based on the physical structure and function of the eye, providing thus a number of salient features that can be potentially employed for the detection of spoofing print-attacks. In our study, a combined dataset was assembled for the investigation of these features, consisting of eye movement recordings and the corresponding iris images collected from 100 subjects. The collected iris images were utilized in direct implementation of iris print-attacks against an eye tracking device. We developed a methodology for the detection of spoof indicative artifacts in the recorded signals, and fed the extracted features from the live and spoof eye signals into a two-class SVM classifier. The obtained results indicate a best correct classification rate (CCR) of 95.7%. Furthermore, we demonstrate the moderate decrease in liveness detection rates during subsampling of the eye movement signal to frequencies as low as 15 Hz. This result indicates the usefulness of running gaze estimation algorithms on existing iris recognition devices where such sampling frequency rate is common.

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Complex eye movement pattern biometrics: Analyzing fixations and saccades

Cited by 63 publications

References 16 publications

Person identification through entropy oriented mean shift clustering of human gaze patterns

Person identification through entropy oriented mean shift clustering of human gaze patterns

Wrist-worn pervasive gaze interaction

Gaze estimation as a framework for iris liveness detection

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