Biometric authentication via oculomotor plant characteristics

Komogortsev, Oleg V.; Karpov, Alex; Price, Larry R.; Aragon, C.

doi:10.1109/icb.2012.6199786

Cited by 53 publications

(29 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…properties of a person's saccades but the inferred properties of their oculomotor plant 30 (Komogortsev et al, 2012). In general, however, it is not well understood why there is such 31 rich variation among people in how they move their eyes, and it is curious that the question 1 has not often been systematically explored outside the non-biometric literature.…”

Section: Saccades and Smooth Pursuitmentioning

confidence: 97%

Individual differences in human eye movements: An oculomotor signature?

et al. 2017

View full text Add to dashboard Cite

2Human eye movements are stereotyped and repeatable, but how specific to a normal 3 individual are the quantitative properties of his or her eye movements? We recorded 4 saccades, anti-saccades and smooth-pursuit eye movements in a sample of over 1000 healthy 5 young adults. A randomly selected subsample (10%) of participants were re-tested on a 6 second occasion after a median interval of 18.8 days, allowing us to estimate reliabilities. 7Each of several derived measures, including latencies, accuracies, velocities, and left-right 8 asymmetries, proved to be very reliable. We give normative means and distributions for each 9 measure and describe the pattern of correlations amongst them. We identify several measures 10 that exhibit significant sex differences. The profile of our oculomotor measures for an 11 individual constitutes a personal oculomotor signature that distinguishes that individual from 12 most other members of the sample of 1000.

show abstract

Section: Saccades and Smooth Pursuitmentioning

confidence: 97%

Individual differences in human eye movements: An oculomotor signature?

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Over the past decade, an emergent sub-field of biometrics has grown around the idea that the unique properties of the oculomotor control system can be interpreted from the measurable properties of eye movements [9,10,14,15]. Therefore, we hypothesize that eye movement biometrics will exhibit patterns that may assist in the diagnosis of mTBI, providing an automated framework with which to reduce the workload of physicians.…”

Section: Motivation and Hypothesismentioning

confidence: 95%

The application of eye movement biometrics in the automated detection of mild traumatic brain injury

Komogortsev

Holland

2014

CHI '14 Extended Abstracts on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

This paper presents a pilot study for the automated detection of mild traumatic brain injury (mTBI) via the application of eye movement biometrics. Biometric feature vectors from multiple paradigms are evaluated for their ability to differentiate subjects diagnosed with mTBI from healthy subjects within a small subject pool. Supervised and unsupervised machine learning techniques were applied to the problem, with preliminary results indicating a potential 100% classification accuracy from a supervised learning technique and 89% classification accuracy from an unsupervised technique.According to reports from the Centers for Disease Control and Prevention [4], approximately 1.7 million people are diagnosed with TBI each year in the United States, of which nearly 75% (or 1.3 million) are incidences of mTBI. This does not account for the undiagnosed occurrences of mTBI that are thought to exceed 25% of the reported figure, or nearly 425,000 undiagnosed cases per year.Each year there are approximately 52,000 TBI-related deaths in the United States, accounting for roughly one-third (30.5%) of all injury-related deaths [4]. mTBI increases the risk of TBI [5], and can cause neurological disorders which persist years after injury [16], affecting thought, behavior, and emotion, producing physical symptoms such as fatigue, nausea, vertigo, headache, lethargy, and blurred vision [11].

show abstract

“…In this way, certain special characteristics of the individuals may be captured and subsequently utilized for their biometrical identification. Recently, their work has been extended in [9]. In [10], Kinnunen et al targeted on the development of a method for the evaluation of eye movement recordings coming from a task independent visual scenario.…”

Section: Previous Workmentioning

confidence: 98%

Human eye movements as a trait for biometrical identification

Rigas

Economou

Fotopoulos

2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

View full text Add to dashboard Cite

This research work proposes an innovative processing scheme for the exploitation of eye movement dynamics on the field of biometrical identification. As the mechanisms that derive eye movements highly depend on each person's idiosyncrasies, cues that reflect at a certain extent individual characteristics may be captured and subsequently deployed for the implementation of a robust identification system. Our methodology involves the employment of a non -parametric statistical test, the multivariate Wald -Wolfowitz test (WWtest), in order to compare the distributions of saccadic velocity and acceleration features, which are extracted while a person fixates on visual stimuli. In the evaluation section we use two publicly available datasets that supply recorded eye movements from a number of subjects during the observation of a moving spot on a computer screen. The resulting identification rates exhibit the efficacy of the suggested scheme to adequately segregate people according to their eye movement traits.

show abstract

Biometric authentication via oculomotor plant characteristics

Cited by 53 publications

References 19 publications

Individual differences in human eye movements: An oculomotor signature?

Individual differences in human eye movements: An oculomotor signature?

The application of eye movement biometrics in the automated detection of mild traumatic brain injury

Human eye movements as a trait for biometrical identification

Contact Info

Product

Resources

About