arXiv Open Access 2020

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

Louise Gillian C. Bautista Prospero C. Naval

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Abstrak

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.

Topik & Kata Kunci

cs.CV cs.LG

Penulis (2)

Louise Gillian C. Bautista

Prospero C. Naval

Format Sitasi

APA MLA BibTeX

Bautista, L.G.C., Naval, P.C. (2020). GazeMAE: General Representations of Eye Movements using a Micro-Macro Autoencoder. https://arxiv.org/abs/2009.02437

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓