Driver drowsiness detection in facial images

Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, drowsy driver alert systems are meant to reduce the main cause of traffic accidents. Different approaches have been developed to tackle with the fatigue detection problem. Though most reliable techniques to asses fatigue involve the use of physical sensors to monitor drivers, they can be too intrusive and are less likely to be adopted by the car industry. A relatively new and effective trend consists on facial image analysis from video cameras that monitor drivers. How to extract effective features of fatigue from images is important for many image processing applications. This project proposes a face descriptor that can be used to detect driver fatigue in static frames. This descriptor represents each frame of a sequence as a pyramid of scaled images that are divided into non-overlapping blocks of equal size. The pyramid of images is combined with three different image descriptors. The final descriptors are filtered out using feature selection and a Support Vector Machine is used to predict the drowsiness state. The proposed method is tested on the public NTHUDDD dataset, which is the state-of-the-art dataset on driver drowsiness detection

Yawn analysis with mouth occlusion detection

tOne of the most common signs of tiredness or fatigue is yawning. Naturally, identification of fatiguedindividuals would be helped if yawning is detected. Existing techniques for yawn detection are centred onmeasuring the mouth opening. This approach, however, may fail if the mouth is occluded by the hand, as itis frequently the case. The work presented in this paper focuses on a technique to detect yawning whilstalso allowing for cases of occlusion. For measuring the mouth opening, a new technique which appliesadaptive colour region is introduced. For detecting yawning whilst the mouth is occluded, local binarypattern (LBP) features are used to also identify facial distortions during yawning. In this research, theStrathclyde Facial Fatigue (SFF) database which contains genuine video footage of fatigued individuals isused for training, testing and evaluation of the system

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy

Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions

Drowsy Driver Detection System (DDDS)

Driver weariness is one of the key causes of road mishaps in the world. Detecting the drowsiness of the driver can be one of the surest ways of quantifying driver fatigue. In this project we have developed an archetype drowsiness detection system. This mechanism works by monitoring the eyes of the driver and sounding an alarm when he/she feels heavy eyed. The system constructed is a non-intrusive real-time perceiving system. The priority is on improving the safety of the driver. In this mechanism the eye blink of the driver is detected. If the driver?s eyes remain closed for greater than a certain period of time, the driver is deemed to be tired and an alarm is sounded. The programming for this is carried out in OpenCV using the Haar cascade library for the detection of facial features

Video Processing Analysis For Non-Invasive Fatigue Detection And Quantification

Fatigue is a common symptom of weakness either physically or mentally. These symptoms may led to a drop in motivation, weakened sensitivity, slowing of responsiveness and inability to give full attention. All of these problems can cause adverse effects, such as accidents, especially those that require full attention as drivers of vehicles, and rail operators, the pilot of an aircraft or ship operators. This research investigates systems to detect and quantify the signs of fatigue using non-invasive facial analytics. There are four main algorithms that represent the major contribution from the PhD research. These algorithms encompass facial fatigue detection and quantification system as a whole. Firstly, a new technique to detect the face is introduced. This face detection algorithm is an affiliation of colour skin segmentation technique, connected component of binary image usage, and learning machine algorithm. The introduced face detection algorithm is able to reduce the false positive detection rate by a very significant margin. For the facial fatigue detection and quantification, the major fatigue signs features are from the eye activity. A new algorithm called the , Interdependence and Adaptive Scale Mean Shift (IASMS) is presented. The IASMS is able to quantify the state of eye as well as to track non-rigid eye movement. IASMS integrates the mean shift tracking algorithm with an adaptive scale scheme, which is used to track the iris and quantify the iris size. The IASMS is associated with face detection algorithm, image enhanced scheme, eye open detection technique and iris detection method in the initialisation process. This proposed method is able to quantify the eye activities that represent the blink rate and the duration of eye closure. The third contribution is yawning analysis algorithm. Commonly yawning is detected based on a wide mouth opening. Frequently however this approach is thwarted by the common human reaction to hand-cover the mouth during yawning. In this research, a new approach to analyse yawning which takes into account the covered mouth is introduced. This algorithm combines with a new technique of mouth opening measurements, covered mouth detection, and facial distortion (wrinkles) detection. By using this proposed method, yawning is still able to detect even though the mouth is covered. In order to have reliable results from the testing and evaluating of the developed fatigue detection algorithm, the real signs of fatigue are required. This research develops a recorded face activities database of the people that experience fatigue. This fatigue database is called as the Strathclyde Fatigue Facial (SFF). To induce the fatigue signs, ethically approved sleep deprivation experiments were carried out. In these experiments twenty participants, and four sessions were undertaken, which the participant has to deprive their sleep in 0, 3, 5, and 8 hours. The participants were subsequently requested to carry out 5 cognitive tasks that are related to the sleep loss. The last contribution of this research is a technique to recognise the fatigue signs. The existing fatigue detection system is based on single classification. However, this work presents a new approach for fatigue recognition which the fatigue is classified into levels. The levels of fatigue are justified based on the sleep deprivation stages where the SFF database is fully used for training, testing and evaluation of the developed fatigue recognition algorithm. This fatigue recognition algorithm is then integrated into a Fatigue Monitoring Tool (FMT) platform. This FMT has been used to test the participant that carried out the tasks as ship crew in shipping bridge simulator

Real-time Intelligent Alert System on Driver’s Hypo-Vigilance Detection Using Template Matching Technique

According to experts, anyone who do not take break after long period of driving task can cause weariness. This system is one of the major approaches for preventing accidents by fatigue detection and distraction detection. Since there are many systems are available for warning the drivers. As fatigue is the main reason for accidents as well as distraction of drivers especially in the highways and rural areas. Because fatigue reduces driver’s perceptions and decision making capability to control the vehicle. This results irritation and the person is no longer paying attention in driving. In this paper, method for face detection and eye tracking from human face image is used. We have discussed method for determining eye template of open eyes and closed eyes. It is based on real-time acquisition of a driver’s face images and template matching method is applied to extract hypo-vigilance symptoms. DOI: 10.17762/ijritcc2321-8169.15025

Driver drowsiness detection in facial images

Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, drowsy driver alert systems are meant to reduce the main cause of traffic accidents. Different approaches have been developed to tackle with the fatigue detection problem. Though most reliable techniques to asses fatigue involve the use of physical sensors to monitor drivers, they can be too intrusive and are less likely to be adopted by the car industry. A relatively new and effective trend consists on facial image analysis from video cameras that monitor drivers. How to extract effective features of fatigue from images is important for many image processing applications. This project proposes a face descriptor that can be used to detect driver fatigue in static frames. This descriptor represents each frame of a sequence as a pyramid of scaled images that are divided into non-overlapping blocks of equal size. The pyramid of images is combined with three different image descriptors. The final descriptors are filtered out using feature selection and a Support Vector Machine is used to predict the drowsiness state. The proposed method is tested on the public NTHUDDD dataset, which is the state-of-the-art dataset on driver drowsiness detection

An eeg based study of unintentional sleep onset

Ph.DDOCTOR OF PHILOSOPH