Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Dewi, Christine; Chen, Rung-Ching; Jiang, Xiaoyi; Yu, Hui

doi:10.7717/peerj-cs.943

Cited by 50 publications

(15 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5. From these points, we calculate the Eye Aspect Ratio (EAR) [44] Refer to ''(1)''. Based on the threshold value, we declare whether either eye is open or closed as in ''(2)''.…”

Section: ) Eye Aspect Ratio (Ear)mentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

View full text Add to dashboard Cite

The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.INDEX TERMS ADAS, driver drowsiness, road safety, driver monitoring, IoT, edge computing, mobile application, road accidents, cloud computing.

show abstract

“…5. From these points, we calculate the Eye Aspect Ratio (EAR) [44] Refer to ''(1)''. Based on the threshold value, we declare whether either eye is open or closed as in ''(2)''.…”

Section: ) Eye Aspect Ratio (Ear)mentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The EAR threshold was set to 0.3 as recommended by the studies in [23] and [24]. If the EAR falls below 0.3 and then rises above it, a blink is registered.…”

Section: Figure 5: the 6 Facial Landmarks Associated With The Eyementioning

confidence: 99%

Machine learning based driver monitoring system: A case study for the Kayoola EVS

Ziryawulawo¹,

Kirabo²,

Mwikirize

et al. 2023

SAIEE Afr. Res. J.

View full text Add to dashboard Cite

With the ever-growing traffic density, the number of road accidents has continued to increase. Finding solutions to reduce road accidents and improve traffic safety has become a top priority for Kiira Motors Corporation, a Ugandan state-owned automotive company. The company seeks to develop intelligent driver assistance systems for its market entry product, the Kayoola EVS bus. A machine learning-based driver monitoring system that would monitor driver drowsiness and send out an alarm in case drowsiness is detected has been developed in an attempt to reduce drowsiness-related accidents. The system consists of a camera positioned in such a way as to keep track of the driver's face. The camera is interfaced with a Raspberry Pi minicomputer which carries out the computations and analysis and when drowsiness is detected, an alarm is triggered. Dangerous driver behavior including distraction and fatigue has long been recognized as the main contributing factor in traffic accidents. This paper therefore presents the development of a driver monitoring system for the Kayoola Electric City Bus to address the increasing occurrences of road accidents. The machine learningbased driver monitoring system is designed to be non-intrusive with continuous real-time operation.

show abstract

“…This information was then compared against a set threshold to determine the occurrence of eye blinks. Dewi, Chen, Jiang, et al (2022) adopted a similar approach. In their work they applied the dlib algorithm (King, 2009)to generic video footage to estimate the aperture of each eye based on six facial landmarks: two for the upper eyelid, two for the lower eyelid, one for the medial canthus, and one for the lateral canthus.…”

Section: Introductionmentioning

confidence: 99%

On Validating a Generic Video-Based Blink Detection System for Cognitive Load Detection

Biondi,

Graf,

Pillai

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

This work aims to validate using a video-based blink detection system for detecting changes in cognitive load. Participants completed a cognitive task with increasing levels of difficulty. Blink rate was recorded via both our video-based system and a scientific-grade eye-tracker. Results showed no differences in the blink rates recorded by the two systems. However, while strong evidence was found that the blink rate recorded through the eye-tracker increased under greater cognitive task demand, no such pattern was observed for the output from the video-based system. Our findings show that the overall performance of the video-based system was comparable with that of the eye-tracker's, but its sensitivity in detecting changes in cognitive load was inferior to that of its counterpart's.

show abstract

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Cited by 50 publications

References 48 publications

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

Machine learning based driver monitoring system: A case study for the Kayoola EVS

On Validating a Generic Video-Based Blink Detection System for Cognitive Load Detection

Contact Info

Product

Resources

About