Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection

Panicker, Arun D; Nair, Madhu S.

doi:10.1007/s12046-017-0728-3

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…The buzzer is sounded if the driver becomes tired and shuts his eyes for longer than a second. Panicker and Nair et al [10] proposed worked based on computer vision methods provides a unique methodology for the detection of open eyes that may be employed sleepiness in the driver studies. A low-resolution camera is used to capture the driver footage in the suggested technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Majeed,

Hussein Ali,

A. Al-Hilali

et al. 2023

Bulletin EEI

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Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations.

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Section: Literature Reviewmentioning

confidence: 99%

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Majeed,

Hussein Ali,

A. Al-Hilali

et al. 2023

Bulletin EEI

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“…In [12], iris-sclera pattern analysis method is used to detect the open eye. In the next stage, PERcentage of eyelid CLOsure (PERCLOS) metrics is utilized to determine the drowsiness state of the driver.…”

Section: Related Workmentioning

confidence: 99%

Drowsiness Detection System Based on Machine Learning Using Eye State

Öztürk

Küçükmanisa

Urhan

2022

Balkan Journal of Electrical and Computer Engineering

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Drowsiness is one of the major causes of driver-induced traffic accidents. The interactive systems developed to reduce road accidents by alerting drivers is called as Advanced Driver Assistance Systems (ADAS). The most important ADAS are Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In this study, an ADAS system based on eye state detection is presented to detect driver drowsiness. First, Viola-Jones algorithm approach is used to detect the face and eye areas in the proposed method. The detected eye region is classified as closed or open by making use of a machine learning method. Finally, the eye conditions are analyzed at time domain with PERcentage of eyelid CLOsure (PERCLOS) metric and drowsiness conditions are determined by Support Vector Machine (SVM), kNN and decision tree classifiers. The proposed methods tested on 7 real people and drowsiness states are detected at 99.77%, 94.35%, and 96.62% accuracy, respectively.

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“…There are several limitations to this study including the use of a driving simulator, study design, the size of the test dataset and the scope of ground truth drowsiness. Panicker and Nair (2017) developed a new drowsiness detection system, which includes three stages. In first stage, elliptical approximation and template matching methods were used for face detection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Panicker and Nair (2017) developed a new drowsiness detection system, which includes three stages. In first stage, elliptical approximation and template matching methods were used for face detection.…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

Sharanabasappa

Nandyal²

2021

IJICC

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PurposeIn order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.Design/methodology/approachThe proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.FindingsThe output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implicationsIn this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.Practical implicationsThis paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.Originality/valueDrowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

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Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection

Cited by 12 publications

References 42 publications

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Drowsiness Detection System Based on Machine Learning Using Eye State

An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

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