Programmable Device Circuits and Systems

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Driver Drowsiness Detection Using Facial Landmark Analysis Technique


Affiliations
1 Department of Computer Science and Engineering, RVCE, Bengaluru, India
     

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Drowsy driving is considered to be one of the major reason for road accidents. Lives of the pedestrians, fellow passengers, as well as neighboring travelers are put to risk when driver tend to fall asleep on the steering wheel while driving. In our proposal, early drowsiness of the driver is detected using computer vision techniques to detect the facial landmarks of the driver’s face and the eye blinking patterns are computed using EAR (Eye Aspect Ratio) formula. First, the facial area of the person is detected and the coordinate points for both left and right eye are obtained. Then, EAR is calculated using these sets of coordinates. Next, this EAR is compared with a threshold to check whether the driver is drowsy or not and accordingly alarm is beeped. Instead of using traditional image processing technique to detect eye closure where it checks for disappearance of the white region of the eyes for a period of time, we use a mathematical formula to detect eye closure and it is more convenient and accurate.


Keywords

Early Drowsiness, Facial Landmarks, Eye Aspect Ratio (EAR).
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  • Dian Artanto , M. Prayadi Sulistyanto, Ign. Deradjad Pranowo , Ervan Erry Pramesta ., “Drowsiness detection system based on eye-closure using a low-cost EMG and ESP8266”, 2017 International Conference on Soft Computing, Intelligent System and Information Technology, IEEE.

  • Tereza Soukupova´ and Jan Cˇ ech, “Real-Time Eye Blink Detection using Facial Landmarks”, 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, February 3–5, 2016, IEEE.

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  • Bhargava Reddy, Ye-Hoon Kim, Sojung Yun, Chanwon Seo, Junik Jang, “Real-time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks”, 2017, Conference on Computer Vision and Pattern Recognition Workshops, IEEE

  • Oraan Khunpisuth, Taweechai Chotchinasri, Varakorn Koschakosai, “Driver Drowsiness Detection Using Eye-Closeness Detection”, Signal Image Technology and Internet Based Systems (SITIS), 2016.

  • Omar Rigane, Karim Abbes, Chokri Abdelmoula, “A Fuzzy Based Method for Driver Drowsiness Detection”, Computer Systems and Applications (AICCSA), 2017 IEEE


Abstract Views: 418

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  • Driver Drowsiness Detection Using Facial Landmark Analysis Technique

Abstract Views: 418  |  PDF Views: 3

Authors

B. N. Ramamani
Department of Computer Science and Engineering, RVCE, Bengaluru, India

Abstract


Drowsy driving is considered to be one of the major reason for road accidents. Lives of the pedestrians, fellow passengers, as well as neighboring travelers are put to risk when driver tend to fall asleep on the steering wheel while driving. In our proposal, early drowsiness of the driver is detected using computer vision techniques to detect the facial landmarks of the driver’s face and the eye blinking patterns are computed using EAR (Eye Aspect Ratio) formula. First, the facial area of the person is detected and the coordinate points for both left and right eye are obtained. Then, EAR is calculated using these sets of coordinates. Next, this EAR is compared with a threshold to check whether the driver is drowsy or not and accordingly alarm is beeped. Instead of using traditional image processing technique to detect eye closure where it checks for disappearance of the white region of the eyes for a period of time, we use a mathematical formula to detect eye closure and it is more convenient and accurate.


Keywords


Early Drowsiness, Facial Landmarks, Eye Aspect Ratio (EAR).

References