Multi-template supervised descent method for face alignment

Ding, Cheng; Tian, Weidong; Geng, Chao; Zhu, Xijing; Peng, Qinmu; Zhao, Zhong-Qiu

doi:10.1016/j.cogsys.2020.09.004

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Coaxial light has a strong ability to reflect into the camera through the eye, while off-axis light has a poor ability to reflect into the camera through the eye, resulting in a bright pupil reaction of one bright pupil position and one dark pupil position in two adjacent images. e position and size of the pupil can be obtained through image difference [8]. Guo used edge detection and Hough circle transformed to locate the features of the driver's mouth for early warning.…”

Section: Literature Reviewmentioning

confidence: 99%

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Driving Detection Based on the Multifeature Fusion

Yang¹,

Lu²

2022

Journal of Control Science and Engineering

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In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance.

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Section: Literature Reviewmentioning

confidence: 99%

“…In model training, k − 1 descent direction R k−1 and deviation b k−1 are calculated as shown in equation (7). e k update calculation of feature point x i is shown in equation (8).…”

Section: Facial Feature Localizationmentioning

confidence: 99%