Yawn analysis with mouth occlusion detection

Ibrahim, Marc; Soraghan, John J.; Petropoulakis, Lykourgos; Caterina, Gaetano Di

doi:10.1016/j.bspc.2015.02.006

Cited by 20 publications

(7 citation statements)

References 17 publications

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“…The same approach was also utilized in [29] by using the CNN model on EEG signals. The authors reported that deep-learning methods achieved better accuracy compare to traditional machinelearning algorithms [21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Abbas¹

2020

IJACSA

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Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save roadside accidents. To help reduce the mortality rate, the "HybridFatigue" DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heartbeat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.

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Section: Related Workmentioning

confidence: 99%

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Abbas¹

2020

IJACSA

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“…The computer vision method mainly uses image processing technology to detect the driver's behavior characteristics to judge the driver's drowsiness. These behavioral features mainly include head movement, blink frequency, eyelid closure, yawning, facial expressions, and the like [9,16]. The performance of this method is greatly affected by the ambient light intensity and other factors.…”

Section: Related Workmentioning

confidence: 99%

dWatch

Tong

Wang

et al. 2020

ACM Trans. Sen. Netw.

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Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more convenient, practical, non-invasive method for drowsiness detection. In this article, we propose a real-time driver drowsiness detection system based on mobile devices, referred to as dWatch, which combines physiological measurements with motion states of a driver to achieve high detection accuracy and low power consumption. Specifically, based on heart rate measurements, we design different methods for calculating heart rate variability (HRV) and sensing yawn actions, respectively, which are combined with steering wheel motion features extracted from motion sensors for drowsiness detection. We also design a driving posture detection algorithm to control the operation of the heart rate sensor to reduce system power consumption. Extensive experimental results show that the proposed system achieves a detection accuracy up to 97.1% and reduces energy consumption by 33%. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile devices;

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“…Azim et al [12] presented a system in which the yawning detection method used Viola-Jones face detection to locate the face, extracted the mouth window, and then searched for the lips through spatial fuzzy c-means clustering. Ibrahim et al [13] proposed a method to detect yawning based on mouth opening, mouth covering and facial feature distortions. Then classification of local binary patterns (LBP) features extracted from the mouth when covered by a hand is used for mouth-covered detection.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Yawning Behaviour in Spontaneous Expressions of Drowsy Drivers

Jie

Mahmoud

Stafford-Fraser

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Driver fatigue is one of the main causes of road accidents. It is essential to develop a reliable driver drowsiness detection system which can alert drivers without disturbing them and is robust to environmental changes. This paper explores yawning behaviour as a sign of drowsiness in spontaneous expressions of drowsy drivers in simulated driving scenarios. We analyse a labelled dataset of videos of sleep-deprived versus alert drivers and demonstrate the correlation between handover-face touches, face occlusions and yawning. We propose that face touches can be used as a novel cue in automated drowsiness detection alongside yawning and eye behaviour. Moreover, we present an automatic approach to detect yawning based on extracting geometric and appearance features of both mouth and eye regions. Our approach successfully detects both hand-covered and uncovered yawns with an accuracy of 95%. Ultimately, our goal is to use these results in designing a hybrid drowsiness-detection system.

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Yawn analysis with mouth occlusion detection

Cited by 20 publications

References 17 publications

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

dWatch

Analysis of Yawning Behaviour in Spontaneous Expressions of Drowsy Drivers

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