Sleepiness Detection System Based on Facial Expressions

Tsuzuki, Yutaka; Mizusako, Motoki; Yasushi, Mitsuo; Hashimoto, Hideki

doi:10.1109/iecon.2019.8927215

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…As for the features used for detecting drowsiness, most of the behaviour-based automatic driver drowsiness detection used eyes as the main features of their models. Head movements [7,8,10,11], other basic facial features such as mouths [4,8,9,12,19,21] and facial expressions [6,7,9], and some other features such as hand gestures [7], respiration signal [13] or even machine-generated features [14] can also be used as well. However, even though it can be as non-intrusive as using basic facial features, detecting drowsiness using respiration signals requires more sophisticated tools such as a thermal camera.…”

Section: Resultsmentioning

confidence: 99%

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Driver Drowsiness Detection Based on Drivers’ Physical Behaviours: A Systematic Literature Review

Caryn

Rahadianti

2021

ComEngApp

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One of the most common causes of traffic accidents is human error. One such factor involves the drowsy drivers that do not focus on the road before them. Driver drowsiness often occurs due to fatigue in long distances or long durations of driving. The signs of a drowsy driver may be detected based on one out of three types of tests; i.e., performance test, physiological test, and behavioural test. Since the physiological and performance tests are quite difficult and expensive to implement, the behavioural test is a good choice to use for detecting early drowsiness. Behaviour-based driver drowsiness detection has been one of the hot research topics in recent years and is still increasingly developing. There are many approaches for behavioural driver drowsiness detection, such as Neural Networks, Multi Layer Perceptron, Support Vector Machine, Vander Lugt Correlator, Haar Cascade, and Eye Aspect Ratio. Therefore, this study aims to conduct a systematic literature review to elaborate on the development and research trends regarding driver drowsiness detection. We hope to provide a good overview of the current state of research and offer the research potential in the future.

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Section: Resultsmentioning

confidence: 99%

“…[ [9] Dlib (library for acquiring facial expressions) Eye Inclination Value (digitise blinks) Frequency analysis (drowsiness detection)…”

Section: Facial Expressionmentioning

confidence: 99%

Driver Drowsiness Detection Based on Drivers’ Physical Behaviours: A Systematic Literature Review

Caryn

Rahadianti

2021

ComEngApp

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“…Driver drowsiness detection techniques fall into three main categories. The first category is the biological feature technique that involves analyzing physiological signals [15], skin temperature, and galvanic skin response (GSR) to measure physical conditions that change with the level of drowsiness or fatigue [16][17][18][19]. The second category is vehicle movement indicator techniques specifically focused on driving applications to detect abnormal driving behavior due to fatigue or drowsiness, such as random braking, lane positioning, abnormal speeding, and abnormal steering.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model

Majeed,

Shafique,

Safran

et al. 2023

Sensors

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Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the ‘yawning’ and ‘no_yawning’ classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model.

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