Real-Time Driver Drowsiness Detection System Using Facial Landmark Analysis

In an era where accidents are a leading cause of death and injury worldwide. Improving driver safety can save countless lives and reduce the human suffering associated with road accidents. Despite technological advancements in vehicle safety features, human error remains a leading cause of traffic accidents. This initiative proposes a system that utilizes real-time video analysis to monitor driver behaviour and identify the level of drowsiness the driver has. This project integrates visual behaviour monitoring and machine learning to enhance driver safety and prevent accidents. Drowsy driving is a significant contributor to road accidents globally, particularly among longdistance drivers such as truck and bus drivers. Recognizing the need for real-time, non- intrusive fatigue detection systems, this project presents a low-cost, webcam-based driver drowsiness detection system leveraging image processing and machine learning techniques. The proposed system captures the driver’s facial features through a webcam and analyzes eye aspect ratio (EAR), mouth opening ratio (MOR), and head position using facial landmark detection. These parameters are processed using machine learning algorithms to determine the driver's level of alertness. Upon detecting signs of drowsiness, the system immediately triggers a visual alert to the driver, thereby potentially preventing accidents. The system emphasizes ease of implementation, portability, and reliability, making it suitable for real-world applications with minimal technical overhead.

Driver drowsiness is one of the leading causes of traffic accidents and has a substantial impact on road safety. Many traffic accidents can be avoided if sleepy drivers were given early warnings. Drowsiness detection systems monitor the driver condition and generate an alarm if drowsiness signs are detected. In this paper, a real-time visual-based driver drowsiness detection system is presented aiming to detect drowsiness by extracting an eye feature called the eye aspect ratio. In the proposed system, which is applied on videos obtained from a public drowsiness detection dataset, the face region is first localized in each frame. Then, the eye region is detected and extracted as the region of interest using facial landmarks detector. Following that, the eye aspect ratio value of each frame is calculated, analyzed, and recorded. Finally, three different classifiers, namely, linear support vector machine, random forest, and sequential neural network, are employed to improve the detection accuracy. Subsequently, the extracted data are classified to determine if the driver's eyes are closed or open. An alarm will then be triggered to alert the drowsy driver if an eye closure is recognized for a specified duration of time.

<p>Changes in the lifestyle of individual, several of disease has emerged due to imbalance of doshas components. Ayurveda practitioners could identify the imbalance of dosha and relate the root cause of imbalance of doshas. Analysis of dosha varies from practitioner to practitioner and it requires well practiced practitioner to identify dosha. To overcome, the darshana method was adopted to automatically identify predominant dosha using facial features such as face, eyes, nose, and mouth and skin color. Computer vision and Image processing techniques were made attempted in Ayurveda domain, for identification of predominate prakriti, age, and gender of the subject. Eye Aspect Ratio (EAR), Nose Aspect Ratio (NAR), Mouth Aspect Ratio (MAR) and skin color was computed based on Euclidean distance to identify on-live predomaint prakriti of an individual. The values of MAR ≤ 0.5,EAR ≤ 0.1,NAR ≤ 0.8 as identified as vata , 0.5 ≥ MAR ≤ 0.6, 0.1 ≥ EAR ≤ 0.2, 0.8 ≥ NAR ≤ 1 as identified as pitta and MAR ≥ 0.6,EAR ≥ 0.2 and NAR ≥ 1 as identified as kapha dosha. With the features MAR, EAR and NAR classification of predominant prakriti was carried out with an accuracy of 87.5% with support vector classifier.</p>

Road accidents usually have many causes, but one that is very common around the world is driver drowsiness. The development of an artificial intelligence-based driver drowsiness detection system has been proposed as a means to prevent these incidents and protect the lives of drivers and other road users. The main objective of this project is to develop a driver drowsiness detection system using artificial intelligence techniques capable of detecting drowsiness in real time and alerting the driver in order to prevent traffic accidents. This system will make it possible to warn drivers and take preventative action before an accident occurs. It will use an approach based on artificial intelligence techniques, in particular machine learning. This AI-based driver drowsiness detection system has the potential to significantly reduce the number of road accidents caused by drowsiness, thereby improving road safety and protecting human life. In order to carry out the development of this project, the XP (Extreme Programming) methodology was used in order to have better management and communication development among the working team.

Driver fatigue is a leading cause of road accidents, particularly during long-distance driving, and necessitates reliable, real-time detection methods to prevent safety compromises. This study presents a drowsiness detection system designed to provide early warnings by analyzing eye-blinking patterns through real-time facial recognition and eye tracking using an in-vehicle camera. The system leverages MediaPipe to extract facial landmarks and compute the Eye Aspect Ratio (EAR), a geometric measure that quantifies eye openness. By employing a threshold-based decision mechanism, the system triggers audible alerts when EAR values indicate signs of drowsiness, such as prolonged or frequent eye closures. Evaluated on a dataset of 667 driver images, the system achieved an accuracy of 85% in drowsiness detection, with an optimal EAR threshold of 0.289 identified for maximizing performance. The approach demonstrates robustness against common real-world challenges, including variations in lighting conditions and camera positioning, offering a non-intrusive, adaptive, and highly reliable solution. By focusing on eye-based biometric features derived from facial landmarks, this system contributes an original and practical advancement in driver monitoring technology, with significant potential to reduce fatigue-related accidents and enhance overall road safety.

Drowsiness detection is critical in many sectors, including transportation, healthcare, and workplace safety, since it may have a substantial influence on human performance and safety. Traditional sleepiness detection approaches are frequently subjective, time intensive, and unsuitable for real-time applications. In recent years, computer vision-based techniques that use eye-related characteristics to identify tiredness have shown promise. The eye aspect ratio, a geometric measure determined from ocular landmarks that indicates the openness or closure of the eyes, is one such trait. We present a sleepiness detection method in this research that blends eye aspect ratio computation with machine learning techniques to provide real-time and accurate drowsiness evaluation. We offer a thorough technique that includes calculating the eye-aspect ratio, extracting features, and classifying them with machine learning methods. The ability of our proposed technique is evaluated using a dataset of eye pictures acquired from individuals under various sleepiness situations. Our experimental findings show that our technique is successful in detecting sleepiness with good accuracy, sensitivity, and specificity. Our suggested method includes applications such as sleepy driving detection, tiredness monitoring in hospital settings, and workplace safety. This research advances the area of sleepiness detection by combining the ocular aspect ratio and machine learning to measure tiredness in real time. KEYWORDS: Drowsiness detection, Eye aspect ratio, Machine learning, Eye tracking, Blink rate, Pupil dilation, Real-time monitoring, Non-invasive, Safety, Transportation

The traffic accident is one of the most frequent cause of death in the world; and an important cause of the traffic accident is the fatigue of the driver, who falls asleep during driving. To overcome this problem in this paper, we propose a real-time driver drowsiness detection system, in which the driver's face region is extracted and introduced into a specific designed shallow convolutional neural network (SS-CNN). The SS-CNN detects the state of driver drowsiness using “eye closure” or “eye open” state. To distinguish between the “eye closed” state caused by normal eye blinking and that caused by drowsiness, the proposed system analyzes consecutive results of the SS-CNN. If the system determines that driver falls asleep, an alarm rings to awake the driver in order to avoid a possible accident. The proposed SS-CNN provides an accuracy of 98.95%, which outperforms the previous works. In the experimental section, we provide several links in which real-time operations of the proposed system can be observed.

Driver drowsiness is a major factor that contributing to road accidents. Several researches are ongoing to detect driver drowsiness, but they suffer from the complexity and cost of the models. This paper introduces a hybrid artificial intelligence (AI)-driven framework integrating deep learning (DL) and machine learning (ML) models for real-time drowsiness detection. The system utilizes a robust DL model to classify driver states based on facial images and support vector machine (SVM) model is trained to develop a cost-efficient yet robust facial landmark detector to extract key features such as eye aspect ratio (EAR) and mouth aspect ratio (MAR). We also introduce a multi-stage decision fusion mechanism that combines convolutional neural network (CNN) probability scores with EAR/MAR thresholds to enhance detection reliability and reduce false positives. Experimental results demonstrate that the proposed model achieves 98% accuracy and F1-score, significantly outperforming traditional DL approaches. Additionally, the SVM-based landmark predictor shows improved efficiency with lower mean squared error (MSE) without having higher computational requirements.

Internet of Things (IoTs) is a technology that links objects and mechanisms to the Internet. IoT devices are deployed in a number of areas within the transportation sector, notably in security and surveillance. The ability to track driving behaviour on the road using IoT can improve driver safety and reduce traffic accidents. In this paper, a prototype system to detect a driver’s drowsiness is proposed. In particular, a facial landmark is used to extract the eye region, the eye aspect ratio, and closure patterns. This system will audibly alert the driver and invoke third party notification if drowsiness of the driver is detected. Through this system, traffic accidents caused by drowsy drivers can be avoided and driving behaviour can be evaluated by relevant personnel or employers.

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

A computer vision-based system called the Drowsiness Sensing Device using OpenCV was designed to identify driver drowsiness. The technology uses video frames from a camera positioned inside a car to identify different sleepiness indicators, including the length of eye closure and head position. The Eye Aspect Ratio (EAR), which aids in trying to assess drowsiness, is determined using the OpenCV library, which is also used to extract feature points and detect eye blinks. The system also has an alarm mechanism that sounds when a certain level of drowsiness is attained, alerting the driver to take the appropriate action. The proposed approach can be possibly employed to reduce the number of accidents occurred due to driver drowsiness. The suggested system is a real-time drowsiness sensing system that makes use of OpenCV to gauge a person's level of drowsiness. The technology employs a camera to take pictures of the driver's face, assessing the features like the mouth and eyes to determine how sleepy they are. The system can identify drowsiness by noticing changes in the eyes, such as drooping eyelids, and mouth movements, such as yawning. When the amount of drowsiness surpasses a predetermined threshold, the system informs the driver by assessing the photos using machine learning techniques. By prompting the driver to take a break, the proposed technology may help prevent accidents brought on by drowsy driving.

An alarming cause of road accidents is driver drowsiness. It leads to severe consequences on an individual and public level. This paper discusses a multimodal driver drowsiness detection system involving Deep Convolutional Neural Networks (CNNs) and eye tracking devices. The system is designed for early warning when drowsiness has not yet saturated in alertness. This system, based on facial feature extraction, movement of the eyeballs, and detection of yawning, can determine fatigue in the driver. Methodology includes employing multiple systems of CNN associated with the Kernelized Correlation Filter which will improve the accuracy of face tracking. Real-time fatigue monitoring with behavioral parameters and facial landmarks is done. The experimental results implied that the system can detect drowsiness with an accuracy of about 92 under different light conditions and other environmental conditions. This advancement in research will improve road safety by reducing the risk of accidents due to drowsiness detection. The system benefits the best long-haul truck drivers, night commuters, and even sleep-deprived people. Future enhancements will include making this applicable to automobile safety systems and adding compliance with adaptive machine learning programs to monitor the individual driver.

Face spoofing poses a major threat to facial recognition–based authentication systems, especially in web-based environments that require lightweight and real-time verification. This study develops a real-time anti-spoofing system that integrates YOLOv8n for classifying four facial categories (real, printed, digital, and mask), combined with blink-based liveness verification using the Eye Aspect Ratio (EAR). Using 400,800 images and 18 videos, two training strategies—pretrained and from scratch—were evaluated. The pretrained model achieved a precision of 99.5%, recall of 98.6%, mAP50 of 99.4%, and mAP50–95 of 90.4%, slightly outperforming the from-scratch model. EAR threshold evaluation showed that a value of 0.17 yielded the best performance with 99.02% accuracy, 100% recall, a FAR of 16.11%, and an FRR of 0%. The proposed integration of YOLOv8n and EAR represents a practical novelty for lightweight, web-based anti-spoofing, providing fast inference and stable real-time performance suitable for modern facial authentication systems.

Objective: The present research proposes to develop a dependable, real-time driver fatigue detection system by integrating facial feature evaluation (eye-blinking, head nodding, pupil size, and yawning) with motion displacement analysis via the Improved Lucas-Kanade Optical Flow. The technology is designed to perform accurately in a variety of real-world scenarios, such as those with different lighting and eyeglasses. To enhance detection reliability, a multi-phase classification strategy that combines deep learning, conventional machine learning, and real-time detection models is employed. Methods: The proposed solution is evaluated using the NTHU dataset and real-time driving footage captured under varying lighting and eyewear conditions. Key features such as pupil size variation, eye-blinking, yawning, and head nodding are extracted using euclidean distances between facial landmarks. Modifications include preprocessing optimization and motion displacement analysis via enhanced Lucas-Kanade optical flow. Performance is assessed using metrics like F1 score, mAP, accuracy, precision, and Euclidean distance, and compared with established algorithms like CNN-ResNet50V2. Findings: The results obtained indicate in terms of accuracy, speed, and resilience, the YOLOv8-FD-based method performs significantly better than conventional models like CNN-ResNet50v2. Even when wearing eyewear, the system consistently recognizes fatigue indications, achieving an accuracy rate of 98.06% on the dataset obtained. Its exceptional performance results from the combination of facial attribute extraction and landmark-based euclidean distance analysis, which guarantees constant accuracy in a variety of situations. Additionally, under difficult circumstances like changing lighting and several kinds of eyewear, the system acquired an F1 score of 97%, mAP of 94%, and precision of 98.5%. Novelty: The novelty of this research lies in combining landmark-based Euclidean distance analysis with multiple facial indicators and employing YOLOv8-FD for real-time fatigue detection. This represents one of the earliest implementations of YOLOv8-FD in this domain. The system's exceptional performance under various eyewear conditions, coupled with its 98.06% accuracy, marks a significant advancement over previous CNN-based approaches, offering a more scalable and reliable solution for driver fatigue detection. Keywords: Fatigue, YOLOV8-FD, Euclidean, CNN, Nodding, Pupil

Driving drowsiness detection through videos/images is one of the most important issues for driver safety in today's world. Because of the great advancements in technology in the last few decades, deep learning techniques applied to computer vision applications such as sleep detection have shown promising results. Drowsiness is characterised by closed eyes, yawning, and micro-sleeps. Moreover, one of the biggest tragedies in the news lately, is toddlers or pets dying from heat built up in cars. In this work, a real-time deep learning algorithm is designed to monitor driver drowsiness, driver distraction, as well as an alert system for forgetting children and pets, and a seat belt usage system. The approach taken was to recognise and localise the face, eyes, and mouth, using the Dlib library, Histogram of Oriented Gradients, and a facial landmark predictor. The eye aspect ratio and the mouth aspect ratio are then calculated and evaluated for yawning detection and micro-sleep detection. The information on the driver's state was saved using a Firebase real-time database. This information is used by the children and pets detection algorithm, which sends an automatic email to the driver if a child or pet is discovered in the backseat when the driver is not in the car. When a driver uses a cell phone, eats, or drinks while driving, this is considered as a distraction. Canny edge detection is used to monitor the seat belt. Furthermore, the proposed method was subjected to several rounds of testing, that proved its viability and reliability.