TY - GEN
T1 - A preliminary study on performance evaluation of multi-view multi-modal gaze estimation under challenging conditions
AU - Kim, Jung Hwa
AU - Jeong, Jin Woo
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/4/25
Y1 - 2020/4/25
N2 - In this paper, we address gaze estimation under practical and challenging conditions. Multi-view and multi-modal learning have been considered useful for various complex tasks; however, an in-depth analysis or a large-scale dataset on multi-view, multi-modal gaze estimation under a long-distance setup with a low illumination is still very limited. To address these limitations, first, we construct a dataset of images captured under challenging conditions. And we propose a simple deep learning architecture that can handle multi-view multi-modal data for gaze estimation. Finally, we conduct a performance evaluation of the proposed network with the constructed dataset to understand the effects of multiple views of a user and multi-modality (RGB, depth, and infrared). We report various findings from our preliminary experimental results and expect this would be helpful for gaze estimation studies to deal with challenging conditions.
AB - In this paper, we address gaze estimation under practical and challenging conditions. Multi-view and multi-modal learning have been considered useful for various complex tasks; however, an in-depth analysis or a large-scale dataset on multi-view, multi-modal gaze estimation under a long-distance setup with a low illumination is still very limited. To address these limitations, first, we construct a dataset of images captured under challenging conditions. And we propose a simple deep learning architecture that can handle multi-view multi-modal data for gaze estimation. Finally, we conduct a performance evaluation of the proposed network with the constructed dataset to understand the effects of multiple views of a user and multi-modality (RGB, depth, and infrared). We report various findings from our preliminary experimental results and expect this would be helpful for gaze estimation studies to deal with challenging conditions.
KW - Deep neural networks
KW - Gaze estimation
KW - Multi-modal interaction
KW - Multi-view learning
UR - http://www.scopus.com/inward/record.url?scp=85090036435&partnerID=8YFLogxK
U2 - 10.1145/3334480.3382856
DO - 10.1145/3334480.3382856
M3 - Conference contribution
AN - SCOPUS:85090036435
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2020 - Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020
Y2 - 25 April 2020 through 30 April 2020
ER -