GazeMAE: General Representations of Eye Movements using a Micro-Macro Autoencoder

Eye movements are intricate and dynamic events that contain a wealth of information about the subject and the stimuli. We propose an abstract representation of eye movements that preserve the important nuances in gaze behavior while being stimuli-agnostic. We consider eye movements as raw position a...

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Bibliographic Details
Main Authors Bautista, Louise Gillian C, NavalJr, Prospero C
Format Journal Article
LanguageEnglish
Published 04.09.2020
Subjects
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Online AccessGet full text
DOI10.48550/arxiv.2009.02437

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Summary:Eye movements are intricate and dynamic events that contain a wealth of information about the subject and the stimuli. We propose an abstract representation of eye movements that preserve the important nuances in gaze behavior while being stimuli-agnostic. We consider eye movements as raw position and velocity signals and train separate deep temporal convolutional autoencoders. The autoencoders learn micro-scale and macro-scale representations that correspond to the fast and slow features of eye movements. We evaluate the joint representations with a linear classifier fitted on various classification tasks. Our work accurately discriminates between gender and age groups, and outperforms previous works on biometrics and stimuli clasification. Further experiments highlight the validity and generalizability of this method, bringing eye tracking research closer to real-world applications.
DOI:10.48550/arxiv.2009.02437