Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye

Jäger, Lena A.; Makowski, Silvia; Prasse, Paul; Liehr, Sascha; Seidler, Maximilian; Scheffer, Tobias

doi:10.1007/978-3-030-46147-8_18

Cited by 34 publications

(34 citation statements)

References 37 publications

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“…where r = 1000 is the sampling rate in Hz. We then perform the "fast" and "slow" transformations proposed in [27]:…”

Section: Data Preprocessingmentioning

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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“…where r = 1000 is the sampling rate in Hz. We then perform the "fast" and "slow" transformations proposed in [27]:…”

Section: Data Preprocessingmentioning

confidence: 99%

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

Lohr¹,

Komogortsev²

2022

Preprint

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“…Recent work uses deep learning, either processing extracted features [27], [28] or learning an embedding end-toend from the raw eye tracking signal [12], [29], [30]. Out of all prior approaches, DeepEyedentification is the only model that is able to utilize micro-movements contained in the raw signal.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…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%

“…The additional information contained in the highfrequency micro-movements bear the potential of considerably speeding up the identification. Recently, a neural network has been studied that processes a raw monocular eye tracking signal measured during reading [12]. This approach does not rely on any prior detection of specific types of macro-or micro-movements.…”

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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Using Eye Movement Features for Secure Authentication

Almohaimeed,

Albriki,

Abdulkreem

et al. 2024

Signals and Communication Technology

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Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye

Cited by 34 publications

References 37 publications

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

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

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

Using Eye Movement Features for Secure Authentication

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