Eye Movements in Biometrics

Kasprowski, Paweł; Ober, Józef

doi:10.1007/978-3-540-25976-3_23

Cited by 140 publications

(90 citation statements)

References 11 publications

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“…Other researchers in the field of eye movement biometrics have reported results as follows: average false acceptance rate (FAR) from 1.4% to 17.5% and average false rejection rate (FRR) from 12.6% to 28.9% [20] for 9 subjects, and an identification rate of 83% for 12 subjects [21]; FAR 5.4% and FRR 56.6% for 41 subjects [22]; an equal error rate (EER) of nearly 30% for 15 subjects [23]; EER around 35% for 173 subjects [24]; the best EER of 10.8% [28] for 200 subjects; accuracy of 63% for 32 subjects [26], the best accuracy of 43.1% for 22 subjects [26]; and an accuracy of 33.3% for 34 subjects [27]. The results obtained in the present research are at least equally good compared with these preceding studies.…”

Section: Discussionmentioning

confidence: 99%

“…Those with less than 30-40 subjects can be seen as preliminary investigations purely on statistical grounds. A researcher with a thorough knowledge of machine learning algorithms could show the weaknesses of how inadequately scarce tests have obviously been implemented in some of the previous research [20][21][22][23][24][25][26][27], and some descriptions of the test procedures have been very narrow. In order to achieve statistically reliable and credible results, the validation task has to be done thoroughly and it must follow the correct principles of testing in machine learning.…”

Section: Discussionmentioning

confidence: 99%

“…Eye movements have been proposed for biometrics using the optic stimulation from a moving spot [20]. Signal analysis has been applied by computing Cepstrum features in order to identify the subject.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Biometrics With Eye Movements

Zhang

Juhola

2017

IEEE J. Biomed. Health Inform.

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Eye movements are a relatively novel data source for biometric identification. When video cameras applied to eye tracking become smaller and more efficient, this data source could offer interesting opportunities for the development of eye movement biometrics. In the present article, we study primarily biometric identification as seen as a classification task of multiple classes, and secondarily biometric verification considered as binary classification. Our research is based on the saccadic eye movement signal measurements from 109 young subjects. In order to te s t the dat a m e as ur e d, w e us e a pr o c e dur e o f biom e tri c identification according to the one-versus-one (subject) principle. In a development from our previous research, which also involved biometric verification based on saccadic eye movements, we now apply another eye movement tracker device with a higher sampling frequency of 250 Hz. The results obtained are good, with correct identification rates at 80-90% at their best.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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On Biometrics With Eye Movements

Zhang

Juhola

2017

IEEE J. Biomed. Health Inform.

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“…The idea that eye movements may be used for human identification is about 10 years old [16] [20]. There have been several publications showing that the method is promising, however it is still on the very early research stage because collecting eye movements' data is difficult and eye movement capturing devices (eye trackers) are still relatively expensive.…”

Section: Human Identification Using Eye Movementsmentioning

confidence: 99%

The Impact of Temporal Proximity between Samples on Eye Movement Biometric Identification

Kasprowski¹

2013

Computer Information Systems and Industrial Management

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Abstract. Eye movements identification is an interesting alternative to other biometric identification methods. It compiles both physiological and behavioral aspects and therefore it is difficult to forge. However, the main obstacle to popularize this methodology is lack of general recommendations considering eye movement biometrics experiments. Another problem is lack of commonly available databases of eye movements. Different authors present their methodologies using their own datasets of samples recorded with different devices and scenarios. It excludes possibility to compare different approaches. It is obvious that the way the samples were recorded influences the overall results. This work tries to investigate how one of the elements -temporal proximity between subsequent measurements -influences the identification results. A dataset of 2556 eye movement recordings collected for over 5 months was used as the basis of analyses. The main purpose of the paper is to identify the impact of sampling and classification scenarios on the overall identification results and to recommend scenarios for creation of future datasets.

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“…For example, gaze trajectory (Deravi & Guness, 2011;Galdi et al, 2013), gaze velocity (Yoon et al, 2014), and pupillary characteristics (Bednarik et al, 2005) have been applied with reasonable success for biometric identification. Kasprowski and Ober (2004) utilized a combination of eye reaction time and stabilization time as features to build a predictive model for biometric identification. They applied 10-fold cross-validation methods to test four predictive models (k-nearest neighbors, naive Bayes, C4.5 decision tree, and support vector machines) on eye tracking data collected from nine participants.…”

Section: Introductionmentioning

confidence: 99%

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Yoon¹,

Alamudun²,

Hudson³

et al. 2018

Journal of Human Performance in Extreme Environments

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Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F 1 -scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.

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Eye Movements in Biometrics

Cited by 140 publications

References 11 publications

On Biometrics With Eye Movements

On Biometrics With Eye Movements

The Impact of Temporal Proximity between Samples on Eye Movement Biometric Identification

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

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