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 [BibTeX] [Marc21]
A Deep Learning Approach for Robust Head Pose Independent Eye Movements Recognition from Videos
Type of publication: Conference paper
Citation: Siegfried_ETRA_2019
Publication status: Accepted
Booktitle: 2019 ACM Symposium on Eye Tracking Research and Applications
Year: 2019
Month: June
Pages: 5
Publisher: ACM
ISBN: 978-1-4503-6709-7/19/06
DOI: 10.1145/3314111.3319844
Abstract: Recognizing eye movements is important for gaze behavior understanding like in human communication analysis (human-human or robot interactions) or for diagnosis (medical, reading impairments). In this paper, we address this task using remote RGB-D sensors to analyze people behaving in natural conditions. This is very challenging given that such sensors have a normal sampling rate of 30 Hz and provide low-resolution eye images (typically 36x60 pixels), and natural scenarios introduce many variabilities in illumination, shadows, head pose, and dynamics. Hence gaze signals one can extract in these conditions have lower precision compared to dedicated IR eye trackers, rendering previous methods less appropriate for the task. To tackle these challenges, we propose a deep learning method that directly processes the eye image video streams to classify them into fixation, saccade, and blink classes, and allows to distinguish irrelevant noise (illumination, low-resolution artifact, inaccurate eye alignment, difficult eye shapes) from true eye motion signals. Experiments on natural 4-party interactions demonstrate the benefit of our approach compared to previous methods, including deep learning models applied to gaze outputs.
Keywords: blink, Convolutional neural network., eye movements, remote sensing, saccade, video processing
Projects Idiap
Authors Siegfried, Remy
Yu, Yu
Odobez, Jean-Marc
Added by: [UNK]
Total mark: 0
  • Siegfried_ETRA_2019.pdf