Enhancing eye-movement-based biometric identification method by using voting classifiers

Kasprowski, Paweł; Ober, Józef

doi:10.1117/12.603321

Cited by 20 publications

(8 citation statements)

References 14 publications

(15 reference statements)

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“…To see if the results are stable for different signal conversions we repeated the same classification experiments on datasets converted using different algorithms previously used in eye movement biometric identification [1] [13]. The results were similar, always showing negative correlation, however for some conversions the correlation was not strong.…”

Section: Resultsmentioning

confidence: 98%

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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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Section: Resultsmentioning

confidence: 98%

“…The problem common for all biometric methods using behavioral traits is so called learning effect [13]. When using the same stimulus for several times the person familiarizes with it and eye movements tend to become automatic.…”

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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“…The eye movement characteristics of eye-movement restricted stimulus mainly represent the properties of the oculomotor plant, because the cognitive strategies from the brain are restricted by the stimulus. In a common free-will visual task, the participant’s reaction time will decrease upon increasing the times for which the participant is watching the same stimulus, which is known as the “learning effect” [ 36 ]. On the other hand, the recognition time should be as short as possible for a practical biometric identification method.…”

Section: Introductionmentioning

confidence: 99%

Biometric recognition via texture features of eye movement trajectories in a visual searching task

Xue

Cheng

et al. 2018

PLoS ONE

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Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers’ temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.

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“…We report the identification results obtained for the implementation of the suggested scheme on two publicly available eye movement datasets -offered by [13]. The particular sets were collected during an experiment wherein each participant observed a point moving at predefined…”

Section: Experimental Evaluationmentioning

confidence: 99%

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)

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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.

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Enhancing eye-movement-based biometric identification method by using voting classifiers

Cited by 20 publications

References 14 publications

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

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

Biometric recognition via texture features of eye movement trajectories in a visual searching task

Human eye movements as a trait for biometrical identification

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