Vision-Based Fatigue Driving Recognition Method Integrating Heart Rate and Facial Features
- 16 March 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Intelligent Transportation Systems
- Vol. 22 (5), 3089-3100
- https://doi.org/10.1109/tits.2020.2979527
Abstract
Driving fatigue can be detected by measuring drivers' heart rate with a wearable device or extracting their facial features with an RGB camera. However, a wearable device causes inconvenience and discomfort to the driver, and an RGB camera's detection accuracy may be affected by light, glasses, and head orientation. Furthermore, most existing methods ignored the temporal information of fatigue features and the relationship between the features, lowering recognition accuracy. Additionally, some existing fatigue detection methods focused on dealing with fatigue features with a temporal slice, ignoring temporal variations in the features. To address these problems, a single RGB-D camera is first used to extract three fatigue features: heart rate, eye openness level, and mouth openness level. More importantly, this paper proposes a novel multimodal fusion recurrent neural network (MFRNN), integrating the three features to improve the accuracy of driver fatigue detection. Specifically, a recurrent neural network (RNN) layer is applied in the MFRNN to obtain the temporal information of the features. Since the heart rate feature is a physiological signal extracted indirectly, it contains more noise and is fuzzier than the other features. To deal with the fuzziness and noise, we combine fuzzy reasoning with RNN to extract the temporal information of the heart rate. To identify the relationship between the features, we develop a new relationship layer containing a two-level RNN, for which the input is the temporal information of the features. Both the simulation and field experiment results show that the proposed method provides better performance than similar methods.Keywords
Funding Information
- Natural Science Foundation of China (61973126, 61863028, 81660299, 61503177)
- Guangdong Natural Science Funds for Distinguished Young Scholar (2017A030306015)
- Pearl River S and T Nova Program of Guangzhou (201710010059)
- Guangdong special projects (2016TQ03 × 824)
- Fundamental Research Funds for the Central Universities (2019ZD27)
- Science and Technology Department of Jiangxi Province of China (20161ACB21007, 20171BBE50071, 20171BAB202033)
- Science and Technology Planning Project of Guangdong Province (2017B090914002)
- Innovation Team of the Modern Agriculture Industry Technology System in Guangdong Province (2019KJ139)
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