Predict Driver Fatigue Using Facial Features

Berkati, Oussama; Srifi, Mohamed Nabil

doi:10.1109/isaect.2018.8618740

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Vision-based scheme records the action information by video, which turns the original three-dimensional information into two-dimensional information [21,22]. To get richer visual information, Elkholy et al used depth cameras to detect abnormality in common daily action performance [23].…”

Section: Vision-based Action Quality Recognitionmentioning

confidence: 99%

Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action

Yu-xi

Zhou

et al. 2020

IET Communications

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A system called WiSDP, which is based on Wi‐Fi signals, to detect whether a Seated Dumbbell Press action is standard by using inexpensive consumer Wi‐Fi devices is proposed. Compared with the scheme based on high speed cameras and wearable sensors, Wi‐Fi devices are insensitive to light and colour, do not need wear any device, and decrease the risk of disclosing privacy. WiSDP senses environment changes through the Channel State Information which is fine‐grained physical layer information comparing to frequently used Received Signal Strength Indicator. Compared to the action recognition, action quality recognition depends on slight differences between a non‐standard action and standard actions, which makes it challenging. The authors propose an improved sliding window algorithm calculating action energy to extract Seated Dumbbell Press actions from the Channel State Information streams, estimate action quality by choosing an appropriate classifier and use Principal Component Analysis and Butterworth low‐pass filter to remove noise. The authors conduct experiments in two different scenarios and the average true positive rate of WiSDP are 94.66% and 95.11%, respectively.

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Section: Vision-based Action Quality Recognitionmentioning

confidence: 99%

Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action

Yu-xi

Zhou

et al. 2020

IET Communications

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“…Savas et al ( 10 ) extracted the percentage of eyelid closure (PERCLOS) and yawning frequency as feature parameters to recognition the fatigue driving state. Berkati et al ( 11 ) extracted the driver's blink rate, blink time, PERCLOS, and other characteristic parameters. They constructed an RBF (Radial Basis Function) neural network to identify the fatigue driving state.…”

Section: Introductionmentioning

confidence: 99%

A real-time driver fatigue identification method based on GA-GRNN

Wang

Zhang²,

Shi³

et al. 2022

Front. Public Health

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It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.

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Predict Driver Fatigue Using Facial Features

Cited by 2 publications

References 22 publications

Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action

Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action

A real-time driver fatigue identification method based on GA-GRNN

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