Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes

Makowski, Silvia; Jäger, Lena A.; Prasse, Paul; Scheffer, Tobias

doi:10.1109/ijcb48548.2020.9304900

Cited by 19 publications

(17 citation statements)

References 37 publications

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“…This paper reports and extends contributions of the prior conference manuscript of Makowski et al [13]. We develop a deep convolutional neural network (CNN) that (a) processes binocular eye tracking signals.…”

Section: Introductionmentioning

confidence: 69%

“…Out of all prior approaches, DeepEyedentification is the only model that is able to utilize micro-movements contained in the raw signal. This work is further extended by Makowski et al [13] to handle binocular data and detect presentation attacks. Prasse et al [31] study the model's susceptibility to decreased tracking resolution.…”

Section: Related Workmentioning

confidence: 94%

“…The spectrum of visual stimuli that have been studied ranges from a static cross [5], images [32], faces [19], [33], [34], text [11], [12], [16], [26], video [35] and various implementations of jumping points [4], [17], [36], [37], [38]. Only a handful of studies evaluate their models on stimuli that have not also been shown to the respective user during enrollment [11], [12], [13], [16], [26], [31], [35]. Repetitions in stimulus sequences, unfortunately, enable an attacker to record the oculomotoric response to the known stimulus, and to perform a replay attack by presenting the recording to the system.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to the content of the conference presentation [13], we study how (e) the size of training population, (f) the data volume per user for training and enrollment, (g) the type of stimuli and (h) the time intervals between sessions affect verification performance.…”

Section: Introductionmentioning

confidence: 99%

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DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks

Makowski

Prasse

Reich

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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We study involuntary micro-movements of both eyes, in addition to saccadic macro-movements, as biometric characteristic. We develop a deep convolutional neural network that processes binocular eye-tracking signals and verifies the viewer's identity. In order to detect presentation attacks, we develop a model in which the movements are a response to a controlled stimulus. The model detects replay attacks by processing both the controlled but randomized stimulus and the ocular response to this stimulus. We acquire eye movement data from 150 participants, with 4 sessions per participant and conduct experiments on this new and legacy data sets with varying tracker precision and sampling rate. We observe that the model detects replay attacks reliably. For identification and identity verification, the model attains substantially lower error rates than prior work. We explore the relationships between training population size, training data volume, types of visual stimuli, number of training and enrollment sessions, interval between enrollment and probe sessions on one hand and the model performance on the other hand.

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Section: Introductionmentioning

confidence: 69%

Section: Related Workmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks

Makowski

Prasse

Reich

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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show abstract

“…This focus is motivated by the specificity and permanence of human eye movements [2]. Eye movement biometrics systems offer notable advantages over alternative modalities, including the ability to support liveness detection [3,4] and spoof-resistant continuous authentication [5]. Eye movements are also well suited for integration within multimodal biometrics systems [6].…”

Section: Introductionmentioning

confidence: 99%

Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics

Lohr¹,

Komogortsev²

2022

Preprint

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Plain convolutional neural networks (CNNs) have been used to achieve state-of-the-art performance in various domains in the past years, including biometric authentication via eye movements. There have been many relatively recent improvements to plain CNNs, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). Although these networks primarily target image processing domains, they can be easily modified to work with time series data. We employ a DenseNet architecture for end-to-end biometric authentication via eye movements. We compare our model against the most relevant prior works including the current state-of-the-art. We find that our model achieves state-of-the-art performance for all considered training conditions and data sets.

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Improving cognitive-state analysis from eye gaze with synthetic eye-movement data

Prasse,

Reich,

Makowski

et al. 2024

Computers & Graphics

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Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes

Cited by 19 publications

References 37 publications

DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks

DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks

Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics

Improving cognitive-state analysis from eye gaze with synthetic eye-movement data

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