A method of detecting driver drowsiness state based on multi-features of face

Wang, Ping; Shen, Lin

doi:10.1109/cisp.2012.6469987

Cited by 25 publications

(14 citation statements)

References 9 publications

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“…Ping Wang and Lin Shen [3] proposed a system to detect face region because of its high correct rate the AdaBoost algorithm is used in 2012. So then the final solution found is that the exact positions of driver's eyes and mouth are placed depending upon their geometric features respectively.…”

Section: A Methods Of Detecting Driver Drowsiness State Based On Mumentioning

confidence: 99%

A Survey “Fatigue Monitoring System”

Pukale¹,

Pandit²,

Londhe³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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This is a fatigue detection system implemented in smartphone. This system will detect the drowsiness of the driver while he is driving. And if the reverie is detected by the system then it will generate an alert. In this the parameters are blinking of eyes, mouth yawning and head shaking. Head shaking is the new parameter which is used in this system. These parameters are calculated to identify driver's weariness. The Canny Active Contour method is used for detecting yawning and the Harr-like technique is used for detecting face and eye blinking.

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Section: A Methods Of Detecting Driver Drowsiness State Based On Mumentioning

confidence: 99%

A Survey “Fatigue Monitoring System”

Pukale¹,

Pandit²,

Londhe³

et al. 2017

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

show abstract

“…To locate the potential eye positions and scales in the image is applied to the located eyes from facial points which are indicated for eyes. There are 6 points for each left and right eyes as shown in fig below The Eye Aspect Ratio is calculated as follows: leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye) ear = (leftEAR + rightEAR) / 2.0 The function eye_aspect_ratio calculated as: def eye_aspect_ratio(eye): A = dist.euclidean(eye [2], eye [6]) B = dist.euclidean(eye [3], eye [5]) C = dist.euclidean(eye [1], eye [4]) ear = (A + B) / (2.0 * C)…”

Section: F Eye Detection and Eye Openness Estimationmentioning

confidence: 99%

“…To acquire a precise and strong gauge of eye receptiveness, a novel combination calculation proposed which adaptively coordinates the consequences of eye transparency estimations on the multi-model eye recognitions for both eyes [2]. In view of the imaginative methods, the framework accomplishes strong execution on the difficult situations where the current methodologies frequently come up short.…”

Section: Introductionmentioning

confidence: 99%

Driver State Alert Control using Head Shoulder Inclination and Facial Landmarks

Ebrahim¹,

Harshima²,

Abahai³

2020

IJEAT

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Driver sleepiness is one in all the most important causes of most of the accidents within the world. Detecting the driver’s eye weariness is the simplest way for detecting the somnolence of the driver. The prevailing systems in the literature cannot discover sleepiness in folks having lagophthalmos (condition that forestalls eyes from closing utterly) and monocular vision (person with one-eyed or sight loss in one eye). To solve this downside a Driver State Alert Control system is projected which makes the use of head-shoulder inclination, face detection, eye detection, emotion recognition, eye openness estimation and blink counts for detecting the sleepiness and collision liability associated with robust emotional factors. The projected framework endlessly analyses the head shoulder inclination and facial lineaments of the driver to alert the driver by activating the alarm once he/she is drowsy or showing emotion unstable to drive. The typical separation between upper eyelid and lower eyelid of adults suffering from lagophthalmos is about 1-5mm. The EAR (Eye Aspect Ratio) is calculated supporting this separately for each eye. Thus, the proposed technique can be used for folks with lagophthalmos and monocular vision. Also, the entities don’t solely rely on blink count to ascertain the sleepiness, collision risks related to robust emotional factors are considered too. The projected system which is enforced with one camera view on open CV and raspberry pi setting illustrates the systems good efficiency in particulars of authentic sleep identification results and thus reduces road mishaps. It is easy to place in any type of vehicle and price effective too.

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“…Hemadri et al [10], Assari et al [11], and Wang [12] have reported several studies considering other facial features. They detected drowsiness using blinking and yawning features with vision techniques.…”

Section: Introductionmentioning

confidence: 97%

Detection of Driver's Drowsy Facial Expression

Nakamura

Maejima

Morishima

2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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We propose a method for the estimation of the degree of a driver's drowsiness on basis of changes in facial expressions captured by an IR camera. Typically, drowsiness is accompanied by falling of eyelids. Therefore, most of the related studies have focused on tracking eyelid movement by monitoring facial feature points. However, textural changes that arise from frowning are also very important and sensitive features in the initial stage of drowsiness, and it is difficult to detect such changes solely using facial feature points. In this paper, we propose a more precise drowsiness-degree estimation method considering wrinkles change by calculating local edge intensity on faces that expresses drowsiness more directly in the initial stage.

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A method of detecting driver drowsiness state based on multi-features of face

Cited by 25 publications

References 9 publications

A Survey “Fatigue Monitoring System”

A Survey “Fatigue Monitoring System”

Driver State Alert Control using Head Shoulder Inclination and Facial Landmarks

Detection of Driver's Drowsy Facial Expression

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