Yawn Detection Research Articles

Multi-Emotion Features Extraction for Driver Distraction Detection

Two major factors contributing to human-induced traffic accidents are driver fatigue and distraction. To reduce the rate of occurrence of car accidents, some studies introduced systems that record biometric measurements, diver behavior, and driver’s physical features while driving. Data obtained from these systems are used to predict the driver concentration states. However, these attention-detection systems face challenges in terms of applicability and accuracy in detecting negatively impacting driving behaviors. Contactless physical feature extraction and integration of deep learning are effective and applicable methods in this context. Therefore, this study proposes a method based on multi-feature processing in conjunction with you only look once (YOLO)-based object detection to classify driver attention. Experiments and validation were conducted using the open-source Yawn detection dataset (YawDD) and National Tsing Hua University drowsy driver detection dataset (NTHU-DDD). The proposed method not only outperforms those of certain studies and achieved high efficiency in detecting multi-emotion features of drivers and assisting driver attention.

Multi‐parameter fusion driver fatigue detection method based on facial fatigue features

AbstractFatigued driving is one of the main causes of traffic accidents. In order to improve the detection speed of fatigue driving recognition, this paper proposes a driver fatigue detection method based on multi‐parameter fusion of facial features. It uses a cascaded Adaboost object classifier to detect faces in video streams. The DliB library is employed for facial key point detection, which locates the driver's eyes and mouth to determine their states. The eye aspect ratio (EAR) is calculated to detect eye closure, and the mouth aspect ratio (MAR) is calculated to detect yawning frequency and count. The detected percentage of eye closure (PERCLOS) value is combined with yawning frequency and count, and a multi‐feature fusion approach is used for fatigue detection. Experimental results show that the accuracy of blink detection is 91% and the accuracy of yawn detection is 96.43%. Furthermore, compared to the models mentioned in the comparative experiments, this model achieves two to four times faster detection times while maintaining accuracy.

FATIGUE DETECTION OF DRIVERS USING MACHINE LEARNING

In this project, the aim is to develop a prototype drowsiness detection system to address driver fatigue, a major cause of accidents worldwide. The system detects driver drowsiness by monitoring eye blinks and yawning. If the driver's eyes remain closed for an extended period or if yawning is detected, an alarm is sounded. Programming for this system is conducted in OpenCV, utilizing the Haar cascade library for face detection and other machine learning libraries to detect facial features. The system operates in real-time and is nonintrusive, prioritizing driver safety without being obtrusive. By effectively monitoring driver alertness and issuing timely warnings, this system contributes significantly to enhancing road safety. Keywords - Driver fatigue, drowsiness detection, eye blink, yawn detection, OpenCV, Haar cascade, machine learning, facial features.

Automatic Driver Drowsiness Detection System

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This has become a common reason for accidents in recent times. Several facial and body gestures are considered signs of drowsiness and fatigue in drivers, including tiredness in the eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks, which is required for the detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text-to-speech synthesiser), which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and contribute to technology with the goal of preventing fatalities caused by road accidents. Over the past ten years, advances in artificial intelligence and computing technologies have improved driver monitoring systems. Several experimental studies have gathered data on actual driver fatigue using different artificial intelligence systems. In order to dramatically improve these systems' real-time performance, feature combinations are used. An updated evaluation of the driver sleepiness detection technologies put in place during the previous ten years is presented in this research. The paper discusses and displays current systems that track and identify drowsiness using various metrics. Based on the information used, each system can be categorised into one of four groups. Each system in this paper comes with a thorough discussion of the features, classification rules, and datasets it employs.

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

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.

Real-Time Driver Drowsiness Detection Using Images

Abstract: The suggested technology intends to reduce the amount of accidents caused by driver sleepiness and exhaustion, hence increasing transportation safety. In recent years, this has been a prevalent cause of accidents. Several expressions and body motions, including exhaustion in the eyes and yawning, are seen as symptoms of sleepiness and fatigue in drivers. These characteristics indicate that the driver's condition is poor. The EAR (Eye Aspect Ratio) calculates the distance ratio between horizontal and vertical eye landmarks, which is essential for detecting sleepiness. A YAWN value is calculated for yawn detection utilising the distance between the lower and upper lips, and the distance is compared to a threshold value. We installed an eSpeak module (text to speech synthesiser) to provide suitable voice notifications when the driver becomes tired or yawns. The suggested method is intended to reduce the number of accidents and to contribute to technology in order to avoid fatalities caused by road accidents.

Drowsiness Detection using Computer Vision

The proposed system aims to enhance transportation safety by reducing the number of accidents caused by driver fatigue and drowsiness, which have become increasingly common. The system analyzes facial and bodily gestures such as eye tiredness and yawning to identify signs of drowsiness and fatigue. Eye aspect ratio (EAR) is used to detect drowsiness by measuring the ratio of distances between horizontal and vertical eye landmarks. Yawn detection is performed by calculating a YAWN value based on the distance between the upper and lower lips and comparing it to a threshold value. The system incorporates an eSpeak module, a text-to-speech synthesizer, to provide appropriate voice alerts when drowsiness or yawning is detected. The ultimate goal of the proposed system is to reduce the number of fatalities resulting from road accidents and contribute to technology advancements. Keywords: Drowsiness, eSpeak module, Eye aspect ratio, Yawn Detection.

Accident Prevention: Driver’s Drowsiness Detection System Using AI Techniques

One of the main causes of traffic accidents is driver fatigue and drowsiness. Globally, they are increasing the number of fatalities and injuries each year. In this paper, main module focuses on how to recognize tiredness, which will assist to decrease accidents and improve roadsafety. This system gathers photos from a live webcam feed, applies machine learning to the image, and determines whether the driver is sleepy. Many facial expressions and bodily movements, including yawning and sleepy eyes, seen as indicators ofsleepiness. The EAR (Eye Aspect Ratio) measures the ratio of distances between the horizontal and vertical eye landmarks to identify sleepiness. The distance between the lower and upper lips used to produce a YAWN value for yawn detection, and the result is compared to a threshold value. We have used a play sound library, which will giveappropriate voice alert messages when the driver is in a drowsy state. A time limit is applied for driving, if the driver exceeds the time limit then the also system gives the alert message. The proposed system madeto decrease the rate of accidents.

Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Two-Stream Spatial-Temporal Graph Convolutional Networks for Driver Drowsiness Detection.

Convolutional neural networks (CNNs) have achieved remarkable performance in driver drowsiness detection based on the extraction of deep features of drivers' faces. However, the performance of driver drowsiness detection methods decreases sharply when complications, such as illumination changes in the cab, occlusions and shadows on the driver's face, and variations in the driver's head pose, occur. In addition, current driver drowsiness detection methods are not capable of distinguishing between driver states, such as talking versus yawning or blinking versus closing eyes. Therefore, technical challenges remain in driver drowsiness detection. In this article, we propose a novel and robust two-stream spatial-temporal graph convolutional network (2s-STGCN) for driver drowsiness detection to solve the above-mentioned challenges. To take advantage of the spatial and temporal features of the input data, we use a facial landmark detection method to extract the driver's facial landmarks from real-time videos and then obtain the driver drowsiness detection result by 2s-STGCN. Unlike existing methods, our proposed method uses videos rather than consecutive video frames as processing units. This is the first effort to exploit these processing units in the field of driver drowsiness detection. Moreover, the two-stream framework not only models both the spatial and temporal features but also models both the first-order and second-order information simultaneously, thereby notably improving driver drowsiness detection. Extensive experiments have been performed on the yawn detection dataset (YawDD) and the National TsingHua University drowsy driver detection (NTHU-DDD) dataset. The experimental results validate the feasibility of the proposed method. This method achieves an average accuracy of 93.4% on the YawDD dataset and an average accuracy of 92.7% on the evaluation set of the NTHU-DDD dataset.

DRIVER DROWSINESS DETECTION FOR BEHAVIORAL ANALYSIS

Driver drowsiness is the leading cause of traffic accidents; thus, drowsiness detection plays an important role in preventing them. By developing an automatic solution for alerting drivers of drowsiness before an accident occurs, the number of traffic accidents could be reduced. As a result, this study suggests a method for detecting drowsiness in real time. Image processing and machine learning are two aspects of the suggested approach. The purpose of the image processing step is to recognize the driver's face and then extract the image of the driver's eyes. This phase employs the Haar face detection method, which takes video as an input and outputs the discovered face. Next, haar is utilized to extract the detective's eye image, which will be used as an input for the machine learning phase. The role of machine learning, in this case, is to classify whether the driver's eyes are closed or open using a support vector machine. If the classification results show that the driver's eye is closed for a predetermined amount of time, the driver's eyes will be regarded closed, and an alarm will be activated to inform the driver. On available benchmark data, the proposed methodology has been tested. The results show that the hybridized image processing approaches with machine learning techniques are accurate and resilient. As a result, it can be concluded that the presented methodology is a viable solution method for detecting driver tiredness in the real world. Keywords- Drowsiness detection: Face detection; Eye detection; Yawn detection; Eye aspect ratio; Haar cascade algorithm.

Driver Drowsiness Detection Using Machine Learning

Abstract: This document is a review report on the research conducted and the project made in the field of computer engineering to develop a system for driver drowsiness detection to prevent accidents from happening because of driver fatigue and sleeping. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project. Whereas the implementation of the project gives the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system. Furthermore, the paper states the overall of the observation made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a saferroad. Keywords: Driver drowsiness, eye detection, yawn detection, blink pattern

Learning State Assessment in Online Education Based on Multiple Facial Features Detection.

Considering that most of online training is not effectively supervised, this article presents an online leaning state assessment approach which combines blink detection, yawn detection, and head pose estimation. Blink detection is realized by computing the eye aspect ratio and the ratio of closed eye frames to the total frames per unit time to evaluate the degree of eye fatigue. Yawn detection is implemented by computing the aspect ratio of the mouth by using the feature points of the inner lip and combining it with the time of opening mouth to distinguish the mouth state. Head pose estimation is first implemented by calculating the head rotation matrix by matching the feature points of 2D face with the 3D face model and then calculating the Euler angle of the head according to the rotation matrix to evaluate the change of the head pose. Especially in yawn detection, we employ the feature points of inner lips in the calculation of the mouth aspect ratio to avoid the impact of lip thickness of various participants. Furthermore, the blink detection, yawn detection, and head pose estimation are first calculated based on the two-dimensional grayscale image of human face, which could reduce the computational complexity and improve the real-time performance of detection. Finally, combining the values of blinking, yawning, and head pose, multiple groups of experiments are carried out to assess the state of different online learners; then, the learning state is evaluated by analyzing the numerical changes of the three characteristics. Experimental results show that our approach could effectively evaluate the state of online learning and provide support for the development of online education.

Smart Alarm System (Drowsiness Detection System Review Paper)

Abstract: With the increase in automative population, accident rates are rising rapidly, one of the major reasons is the state of drowsiness or fatigue. Such fatal incidents can be prevented if the driver is warned in time. With time, several drowsiness alarm systems are been implemented, the system monitors driver’s movements through various techniques, one of the most considerable one is face detection, Open Computer Vision is widely used to detect driver’s movements for a long period of time. A comparative study on different types of approach is summarized in this paper. The main agenda is to help the society to understand more about the techniques and find the most convenient method for them. Keywords: Drowsiness, Face Detection, Mouth Yawn Detection, Open Computer Vision

Real-Time Driver Drowsiness Detection using Computer Vision

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including tiredness in eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.

Driver Drowsiness and Yawn Detection

Driver Drowsiness and Yawn Detection

On the link between emotional contagion and contagious yawning

Palagi et al. (Neurosci. Biobehav. Rev. 111:149-165, 2020) propose a multidisciplinary approach to the study of spontaneous mimicry, focusing on comparative research on facial mimicry and contagious yawning. In doing so, the authors highlight connections between these behaviors and emotional contagion. The aim of this commentary is to critically evaluate the link between contagious yawning and emotional contagion, address the role of attention in contagious yawning, and promote further research examining the sensory and perceptual factors driving variability in contagious yawning. Contrary to the position of Palagi et al., a strong matching between emotional contagion and contagious yawning has not been established. Variation in contagious yawning appears to be driven by biased attentional processes and yawn detection rather than emotional sharing.

Driver’s fatigue recognition based on yawn detection in thermal images

The paper presents a novel approach to driver’s fatigue recognition based on yawn detection using thermal imaging. This work writes into the active drivers’ assisting systems which can warn on driver’s drowsiness based on continuous observations. The method can operate in day and night conditions without distracting a driver due to usage of thermal images. At first, face alignment is done by detection of eye corners. Then, yawns are detected based on the proposed yawning thermal model. For quantitative evaluation an annotated image database was created which has been made publicly accessible. Experiments were performed in laboratory, as well as in real car conditions, showing high efficacy of the proposed method.

Driver Fatigue Detection and Alert System using Non-Intrusive Eye and Yawn Detection

Driver Fatigue Detection and Alert System using Non-Intrusive Eye and Yawn Detection

Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor.

Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head pose on top of the Viola and Jones (VJ) Haar-like face detector. Several appearance and depth-based feature types are employed for the pose estimation, where comparisons between them in terms of accuracy and speed are presented. It is clearly shown through this work that using the depth data, we improve the accuracy of the head pose estimation. Additionally, we can spot positive detections, faces in profile views detected by the frontal model, that are wrongly cropped due to background disturbances. We introduce a new depth-based feature descriptor that provides competitive estimation results with a lower computation time. Evaluation on a benchmark Kinect database shows that the histogram of oriented gradients and the developed depth-based features are more distinctive for the head pose estimation, where they compare favorably to the current state-of-the-art approaches. Using a concatenation of the aforementioned feature types, we achieved a head pose estimation with average errors not exceeding for pitch, yaw and roll angles, respectively.