Open/Closed Eye Analysis for Drowsiness Detection

Tabrizi, Pooneh R.; Zoroofi, Reza Aghaeizadeh

doi:10.1109/ipta.2008.4743785

Cited by 54 publications

(34 citation statements)

References 12 publications

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“…Then, the connected component analysis was used to increase the accuracy of eye detection. Roshani Tabrizi et al [41] proposed an eye detection algorithm that detects eye in the HSV color space. They also proposed another method in [42] that is based on the method proposed in [43] for the precise eye detection.…”

Section: Methods Based On Binarizationmentioning

confidence: 99%

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A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

Sigari¹,

Pourshahabi²,

Soryani³

et al. 2014

IJAST

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Section: Methods Based On Binarizationmentioning

confidence: 99%

“…In the systems presented in [41,42], driver drowsiness was detected by applying a constant threshold on PERCLOS. In [68], the first stage was driver face identification and then an appropriate threshold was chosen for the system based on physical and psychological characteristics of the identified driver.…”

Section: Methods Based On Thresholdmentioning

confidence: 99%

A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

Sigari¹,

Pourshahabi²,

Soryani³

et al. 2014

IJAST

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show abstract

“…A great number of fatalities occurring in traffic accidents could be avoided if driver drowsiness is detected. Usually, there are three different types of approaches for drowsiness detection [1]: biological indicators, driving behavior and face analysis [2].…”

Section: Introductionmentioning

confidence: 99%

Eye state detection based on nonnegative sparse coding feature

Chen¹,

Jianzhao²

2014

2014 4th IEEE International Conference on Information Science and Technology

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It is very useful to detect our eye open or close state in some sistuations. For example, it will give much help to the drivers for driving with drowsiness. In this paper, we propose an efficient method which is made up of four steps for eye state detection: Eye Localization, Preprocessing, Nonnegative sparse coding feature, SVM classifier. We compared our method to other methods[6][7] in our collected dataset which contains the eyes with glasses or in darkness, and so on. In our method, we will preprocess the image for eliminating the illumination and then analyze the eye image feature based on nonnegative sparse coding. It is proved to be very effective in our dataset.

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“…A large number of them, e.g. [3], [10], [17], [18], [22], [24], employ color-based segmentation techniques, while a signifi cant number of other approaches relies on the Viola-Jones detector, e.g. [7], [9], [13], [21], [27], [28], as shown in Table I.…”

Section: Introductionmentioning

confidence: 99%

Driver alert state and fatigue detection by salient points analysis

Jimenez-Pinto

Torres-Torriti

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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Assessing a driver's state of awarness and fatigue is especially important to reduce the number of traffic accidents often involving bus and truck drivers, who must work during several hours under monotonous road conditions. Two main challenges arise in resolving the state of alert: first, the system must be capable of detecting the driver's face location; secondly, the driver's facial cues, such as blinking, yawning, and eyebrow rising must be recognized. Our approach combines the wellknown Viola-Jones face detector with motion analysis of ShiTomasi salient features within the face to determine the driver's state of alert. The location of the eyes and blinking are cues whose detection is also important. To this end, the proposed method takes advantage of the high reflectivity of the retina to near infrared illumination employing a camera with an 850 nm wavelength filter. Motion analysis of the salient points, in particular cluster mass centers and spatial distribution, has proved successful in determining the driver's state of alert.

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Open/Closed Eye Analysis for Drowsiness Detection

Cited by 54 publications

References 12 publications

A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

Eye state detection based on nonnegative sparse coding feature

Driver alert state and fatigue detection by salient points analysis

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