The Second Eye Movements Verification and Identification Competition

Kasprowski, Paweł; Harężlak, Katarzyna

doi:10.1109/btas.2014.6996285

Cited by 31 publications

(22 citation statements)

References 12 publications

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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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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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“…The same algorithm also achieved the highest classication accuracy in the Second Eye Movements Identication and Verication Competition. 12 An approximation of the Wald-Wolfowitz (WW) test was used to compare large samples, where the WW test statistics were used as similarities in a kNN classier. Because of its computational eciency, the approximate multivariate Wald-Wolfowitz (AMWW) test 4 is also used in this work (see Appendix B).…”

Section: Related Workmentioning

confidence: 99%

Identifying Bitcoin users by transaction behavior

Monaco

2015

SPIE Proceedings

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Digital currencies, such as Bitcoin, oer convenience and security to criminals operating in the black marketplace.Some Bitcoin marketplaces, such as Silk Road, even claim anonymity.1 This claim contradicts the ndings in this work, where long term transactional behavior is used to identify and verify account holders. Transaction features, such as timestamp, coin-ow, and connectivity, contribute to revealing the account-holder's identity.The time between successive transactions is the result of low-frequency eects, such as the desire purchase an item and daily schedule, as well as higher frequency eects, such as hardware and network latency. In addition to transaction time-intervals, dynamic network features of each transaction, such as coin ow and number of edge outputs and inputs, can also be used to identify account-holders. In this paper, we propose novel methodology for identifying and verifying Bitcoin users based on the observation of Bitcoin transactions over time. The behavior we attempt to quantify occurs in the social band of Newell's time scale and is low-frequency compared to other behavioral biometrics. A subset of Blockchain 230686 is analyzed, selecting users that initiated between 100 and 1000 unique transactions per month for at least 6 dierent months. This dataset shows evidence of being nonrandom and nonlinear, thus a dynamical systems approach is taken. Identication and verication accuracies are obtained using monthly Bitcoin samples. Outgoing transactions, as well as both outgoing and incoming transactions, are considered. Results show an inherent lack of anonymity by exploiting patterns in long-term transactional behavior.

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“…This is likely due to periods of time where the user has already gazed at the light, but is still waiting for the position of the active LED to change. Finally, the authors propose, organize and describe two yearly competitions in eye movements verification and identification using their datasets [20,21], which have further increased the research interest in gaze-based authentication.…”

Section: Related Workmentioning

confidence: 99%

Using Reflexive Eye Movements for Fast Challenge-Response Authentication

Sluganovic

Roeschlin

Rasmussen

et al. 2016

Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

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Eye tracking devices have recently become increasingly popular as an interface between people and consumer-grade electronic devices. Due to the fact that human eyes are fast, responsive, and carry information unique to an individual, analyzing person's gaze is particularly attractive for effortless biometric authentication. Unfortunately, previous proposals for gaze-based authentication systems either suffer from high error rates, or require long authentication times.We build upon the fact that some eye movements can be reflexively and predictably triggered, and develop an interactive visual stimulus for elicitation of reflexive eye movements that supports the extraction of reliable biometric features in a matter of seconds, without requiring any memorization or cognitive effort on the part of the user. As an important benefit, our stimulus can be made unique for every authentication attempt and thus incorporated in a challenge-response biometric authentication system. This allows us to prevent replay attacks, which are possibly the most applicable attack vectors against biometric authentication.Using a gaze tracking device, we build a prototype of our system and perform a series of systematic user experiments with 30 participants from the general public. We investigate the performance and security guarantees under several different attack scenarios and show that our system surpasses existing gaze-based authentication methods both in achieved equal error rates (6.3%) and significantly lower authentication times (5 seconds).

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The Second Eye Movements Verification and Identification Competition

Cited by 31 publications

References 12 publications

Application of Dimensionality Reduction Methods for Eye Movement Data Classification

Application of Dimensionality Reduction Methods for Eye Movement Data Classification

Identifying Bitcoin users by transaction behavior

Using Reflexive Eye Movements for Fast Challenge-Response Authentication

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