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Pre-AttentiveGaze: gaze-based authentication dataset with momentary visual interactions.

Junryeol Jeon¹, Yeo-Gyeong Noh¹, JooYeong Kim¹

¹Gwangju Institute of Science of Technology, School of Integrated Technology, Gwangju, 61005, Republic of Korea.

Scientific Data

|February 13, 2025

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This study introduces the Pre-AttentiveGaze dataset for rapid, individual identification using eye movements. Machine learning models validated its effectiveness for pre-attentive gaze-based authentication.

Area of Science:

Computer Science
Biometrics
Human-Computer Interaction

Background:

Gaze-based authentication requires rapid responses for practical application.
Existing methods may not fully leverage the speed of pre-attentive visual processing.

Purpose of the Study:

To introduce and validate the Pre-AttentiveGaze dataset for rapid, individual identification.
To explore the efficacy of pre-attentive processing in gaze-based authentication.

Main Methods:

Collected 76,840 eye movement samples from 34 participants across five sessions.
Extracted and pre-processed gaze features from the collected data.
Validated the dataset using machine learning models.

Main Results:

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Demonstrated the dataset's efficacy in distinguishing individuals based on gaze patterns.
Showcased the potential for rapid authentication using pre-attentive visual stimuli.

Conclusions:

The Pre-AttentiveGaze dataset is effective for gaze-based authentication.
Pre-attentive processing holds significant potential for developing faster biometric systems.