Complex Eye Movement Pattern Biometrics: The Effects of Environment and Stimulus

Holland, Corey D.; Komogortsev, Oleg V.

doi:10.1109/tifs.2013.2285884

Cited by 47 publications

(29 citation statements)

References 23 publications

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“…Minimal spanning trees for graphs were constructed, which were used for biometric identification and also applied statistical methods. Moreover, there are somewhat similar features [24] computed from saccadic eye movements to those in our prior research [14][15][16][17][18][19] and also employed in the present research. Twenty-two, 32, and 173 subjects were measured using three different set-ups [24].…”

Section: Introductionmentioning

confidence: 69%

“…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%

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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: Introductionmentioning

confidence: 69%

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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“…With the current issues in global security, we have witnessed a rapid growth in biometrics applications based on various modalities, which include palm patterns with high spectral wave [2], patterns of eye movement [3], patterns in the electrocardiogram (ECG) [4], and otoacoustic emissions [5]. Each Takashi Nakamura, Valentin Goverdovsky and Danilo P. Mandic are with Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom, {takashi.nakamura14, goverdovsky, d.mandic}@imperial.ac.uk such biometric modality has its strengths and weaknesses, and typically suits only a chosen type of application and its corresponding scenarios [6].…”

Section: Introductionmentioning

confidence: 99%

In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication

Nakamura

Goverdovsky

Mandic

2018

IEEE Trans.Inform.Forensic Secur.

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The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a 'one-fits-all' viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.• Recording scalp-EEG with multiple electrodes is timeconsuming to set-up and cumbersome to wear. Such a

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“…An objective evaluation of the eye tracking effects, its specification and stimulus presentation on the biometric viability of complex eye movement patterns was presented in [1]. Acceptable conditions under which to capture eye movement data is calculated based on Six spatial accuracy tiers (0.5°, 1.0°, 1.5°, 2.0°, 2.5°, 3.0°), six temporal resolution tiers (1000, 500, 250, 120, 75, 30 Hz), and five stimulus types (simple, complex, cognitive, textual, random) .…”

Section: Related Workmentioning

confidence: 99%

Hand Geometry Recognition based on optimized K-Means Clustering and Segmentation Algorithm

Saravanan¹

2013

IJMSR

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-Biometrics plays an important role in electronic authentication based on biological features of the human beings like hand geometry, palm print, finger print, retina and face geometry. The hardware requirements of the different electronic systems that are subset of biometric systems of categories mentioned above vary significantly. The power consumption increases with the hardware complexity used. Large power consumption leads to less portability which is not desired. Most biometric recognition systems require complex optical systems for properly capturing data without noise. The digital pre-processing of input image is an added advantage. Hand Geometry Recognition is the simplest and robust recognition system among all other recognition methods available. Hand geometry recognition involves extracting the hand which is the foreground from the background using segmentation process. Several segmentation algorithms are available. K-Means algorithm is one of the algorithms. In this work the K-Means algorithm is optimized to be used in the recognition process.

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Complex Eye Movement Pattern Biometrics: The Effects of Environment and Stimulus

Cited by 47 publications

References 23 publications

On Biometrics With Eye Movements

On Biometrics With Eye Movements

In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication

Hand Geometry Recognition based on optimized K-Means Clustering and Segmentation Algorithm

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