First eye movement verification and identification competition at BTAS 2012

Kasprowski, Paweł; Komogortsev, Oleg V.; Karpov, Alex

doi:10.1109/btas.2012.6374577

Cited by 41 publications

(31 citation statements)

References 30 publications

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“…To our best knowledge it is the first paper that analyses that aspect of eye movements' biometrics, however the problem has been already introduced in [15].…”

Section: Human Identification Using Eye Movementsmentioning

confidence: 99%

“…The First Eye Movement Verification and Identification Competition (EMVIC) organized in 2012 as an official BTAS conference competition was the first opportunity to compare different approaches [15]. The aim of the competition was to correctly identify individuals on the basis of their eye movements.…”

Section: Eye Movement Verification and Identification Competitionmentioning

confidence: 99%

“…Another reason could be binocular data in A and B (which was already studied in [26] and [21]). In [15] authors suggested also that low quality of data in A and B could be the reason of better performance. In this paper we investigate another of the possible reasons: impact of proximity between consecutive samples.…”

Section: Eye Movement Verification and Identification Competitionmentioning

confidence: 99%

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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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“…To our best knowledge it is the first paper that analyses that aspect of eye movements' biometrics, however the problem has been already introduced in [15].…”

Section: Human Identification Using Eye Movementsmentioning

confidence: 99%

Section: Eye Movement Verification and Identification Competitionmentioning

confidence: 99%

Section: Eye Movement Verification and Identification Competitionmentioning

confidence: 99%

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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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“…Having stated the recognition results, it is worth mentioning here that the disuse of standardized databases and experimental protocol as described in Ref. 16, is a major limitation in the meaningful comparison of the experimental results.…”

Section: Discussionmentioning

confidence: 99%

A Survey of Automatic Person Recognition Using Eye Movements

Saeed

2014

Int. J. Patt. Recogn. Artif. Intell.

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The human eye is rich in physical and behavioral attributes that can be used for automatic person recognition. The physical attributes such as the iris attracted early attention and yielded signi¯cant recognition results, but like most physical biometrics, they have several disadvantages such as intrusive acquisition, vulnerability to spoo¯ng attacks, etc. Consequently, during the last decade the behavioral attributes extracted from human eyes have steadily gained interest from the automatic person recognition research community. In this¯rst of its kind survey, we present the studies utilizing the behavioral attributes of human eyes for automatic person recognition. We have proposed a unique classi¯cation based on the type of stimuli used to elicit behavioral attributes. In addition, for each approach we have carefully examined the common steps involved in automatic person recognition from database acquisition, feature extraction to classi¯cation. Lastly, we also present a comparison of the recognition results obtained by each approach.

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“…Eye movement is known to reveal a lot of interesting information about a human being and eye movement based identification is yet another biometric possibility which was initially proposed about 10 years ago [13]. Since then, many research have been done in that field and the BioEye competition follows the previously announced EMVIC2012 [12] and EMVIC2014 [11] competitions.…”

Section: Description Of Datamentioning

confidence: 98%

Application of Dimensionality Reduction Methods for Eye Movement Data Classification

Gruca

Harężlak

Kasprowski

2015

Advances in Intelligent Systems and Computing

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In this paper we apply two data dimensionality reduction methods to eye movement dataset and analyse how the feature reduction method improves classification accuracy. Due to the specificity of the recording process, eye movement datasets are characterized by both big size and high-dimensionality that make them difficult to analyse and classify using standard classification approaches. Here, we analyse eye movement data from BioEye 2015 competition and to deal with the problem of high dimensionality we apply SVM combined with PCA feature extraction and random forests wrapper variable selection. Our results show that the reduction of the number of variables improves classification results. We also show that some of classes (participants) can be classified (recognised) with high accuracy while others are very difficult to be correctly identified.

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First eye movement verification and identification competition at BTAS 2012

Cited by 41 publications

References 30 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

A Survey of Automatic Person Recognition Using Eye Movements

Application of Dimensionality Reduction Methods for Eye Movement Data Classification

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