TY - GEN
T1 - A Multi-Modal Approach for Driver Gaze Prediction to Remove Identity Bias
AU - Yu, Zehui
AU - Huang, Xiehe
AU - Zhang, Xiubao
AU - Shen, Haifeng
AU - Li, Qun
AU - Deng, Weihong
AU - Tang, Jian
AU - Yang, Yi
AU - Ye, Jieping
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - Driver gaze prediction is an important task in Advanced Driver Assistance System (ADAS). Although the Convolutional Neural Network (CNN) can greatly improve the recognition ability, there are still several unsolved problems due to the challenge of illumination, pose and camera placement. To solve these difficulties, we propose an effective multi-model fusion method for driver gaze estimation. Rich appearance representations, i.e. holistic and eyes regions, and geometric representations, i.e. landmarks and Delaunay angles, are separately learned to predict the gaze, followed by a score-level fusion system. Moreover, pseudo-3D appearance supervision and identity-adaptive geometric normalization are proposed to further enhance the prediction accuracy. Finally, the proposed method achieves state-of-the-art accuracy of 82.5288% on the test data, which ranks 1st at the EmotiW2020 driver gaze prediction sub-challenge.
AB - Driver gaze prediction is an important task in Advanced Driver Assistance System (ADAS). Although the Convolutional Neural Network (CNN) can greatly improve the recognition ability, there are still several unsolved problems due to the challenge of illumination, pose and camera placement. To solve these difficulties, we propose an effective multi-model fusion method for driver gaze estimation. Rich appearance representations, i.e. holistic and eyes regions, and geometric representations, i.e. landmarks and Delaunay angles, are separately learned to predict the gaze, followed by a score-level fusion system. Moreover, pseudo-3D appearance supervision and identity-adaptive geometric normalization are proposed to further enhance the prediction accuracy. Finally, the proposed method achieves state-of-the-art accuracy of 82.5288% on the test data, which ranks 1st at the EmotiW2020 driver gaze prediction sub-challenge.
KW - appearance representation
KW - driver gaze prediction
KW - geometric representation
KW - identity bias
KW - model fusion
UR - http://www.scopus.com/inward/record.url?scp=85096677530&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096677530&partnerID=8YFLogxK
U2 - 10.1145/3382507.3417961
DO - 10.1145/3382507.3417961
M3 - Conference contribution
AN - SCOPUS:85096677530
T3 - ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction
SP - 768
EP - 776
BT - ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction
PB - Association for Computing Machinery, Inc
T2 - 22nd ACM International Conference on Multimodal Interaction, ICMI 2020
Y2 - 25 October 2020 through 29 October 2020
ER -