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Article

Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks

1
Graduate School of Creative Science and Engineering, Waseda University, Tokyo 169-8555, Japan
2
Research Institute for Science and Engineering (RISE), Waseda University, Tokyo 162-0044, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Dieter Schramm and Philipp Sieberg
Sensors 2022, 22(15), 5857; https://doi.org/10.3390/s22155857
Received: 30 June 2022 / Revised: 29 July 2022 / Accepted: 1 August 2022 / Published: 5 August 2022
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
Estimating the driver’s gaze in a natural real-world setting can be problematic for different challenging scenario conditions. For example, faces will undergo facial occlusions, illumination, or various face positions while driving. In this effort, we aim to reduce misclassifications in driving situations when the driver has different face distances regarding the camera. Three-dimensional Convolutional Neural Networks (CNN) models can make a spatio-temporal driver’s representation that extracts features encoded in multiple adjacent frames that can describe motions. This characteristic may help ease the deficiencies of a per-frame recognition system due to the lack of context information. For example, the front, navigator, right window, left window, back mirror, and speed meter are part of the known common areas to be checked by drivers. Based on this, we implement and evaluate a model that is able to detect the head direction toward these regions having various distances from the camera. In our evaluation, the 2D CNN model had a mean average recall of 74.96% across the three models, whereas the 3D CNN model had a mean average recall of 87.02%. This result show that our proposed 3D CNN-based approach outperforms a 2D CNN per-frame recognition approach in driving situations when the driver’s face has different distances from the camera. View Full-Text
Keywords: driver monitoring; gaze classification; convolutional neural networks driver monitoring; gaze classification; convolutional neural networks
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MDPI and ACS Style

Lollett, C.; Kamezaki, M.; Sugano, S. Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks. Sensors 2022, 22, 5857. https://doi.org/10.3390/s22155857

AMA Style

Lollett C, Kamezaki M, Sugano S. Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks. Sensors. 2022; 22(15):5857. https://doi.org/10.3390/s22155857

Chicago/Turabian Style

Lollett, Catherine, Mitsuhiro Kamezaki, and Shigeki Sugano. 2022. "Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks" Sensors 22, no. 15: 5857. https://doi.org/10.3390/s22155857

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