Title: Classification of visual attention by microsaccades using machine learning

Authors: Soichiro Yokoo; Nobuyuki Nishiuchi; Kimihiro Yamanaka

Addresses: Graduate School of Systems Design, Faculty of Computer Science, Tokyo Metropolitan University, Hino, Tokyo, Japan ' Graduate School of Systems Design, Faculty of Computer Science, Tokyo Metropolitan University, Hino, Tokyo, Japan ' Graduate School of Natural Science, Faculty of Intelligence and Informatics, Konan University, Kobe, Hyogo, Japan

Abstract: This paper proposes machine learning methods for classifying visual attention. Eye-tracking data contains a range of useful information related to human visual behaviour. In particular, many recent studies have shown a relationship between visual attention and microsaccades, a type of fixational eye movement. In this study, eye movement and pupil diameter were measured under three controlled experimental conditions requiring different visual attention levels. Microsaccades were extracted from eye-tracking data that included rapid saccades. Various machine learning methods were then used on parameters related to the extracted microsaccades to classify the level of visual attention. By cross-validating data from one participant (test data) with that from other participants (training data), we showed that the support vector machine method had the highest correct discrimination rate (77.1%). These results suggest that it is possible to classify visual attention based on microsaccades.

Keywords: microsaccade; machine learning; visual attention; pupil diameter; eye-tracking.

DOI: 10.1504/IJBM.2024.138229

International Journal of Biometrics, 2024 Vol.16 No.3/4, pp.399 - 418

Received: 20 Mar 2023
Accepted: 02 Aug 2023
Published online: 30 Apr 2024 *

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