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
Published in2020 25th International Conference on Pattern Recognition (ICPR) pp. 7004 - 7011
Main Authors Bautista, Louise Gillian C., Naval, Prospero C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.01.2021
Subjects
Biometrics (access control)
Convolution
Feature extraction
Fitting
Gaze tracking
Pattern recognition
Task analysis
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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 macroscale 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 classification. Further experiments highlight the validity and generalizability of this method, bringing eye-tracking research closer to real-world applications.
DOI:10.1109/ICPR48806.2021.9412761