Visual based drowsiness detection using facial features

Quang Nguyen; Le T. Anh Tho; Toi Vo Van; Hui Yu; Nguyen Duc Thang

doi:10.1007/978-981-10-4361-1_124

Visual based drowsiness detection using facial features

Quang Nguyen
, Le T. Anh Tho
, Toi Vo Van
, Hui Yu
, Nguyen Duc Thang

Vietnam National University Ho Chi Minh City

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

286 Downloads (Pure)

Abstract

In this paper, a camera-based system is proposed to detect and monitor drowsiness of a car driver in real time. The system utilizes an RGB image to track the drivers’ face and their eyes to detect sleepy sign. For the face detection and segmentation, a robust method based on Haar features is applied. Within the segmented areas of faces, random forest is utilized to locate eye regions. Once the eyes are located, the local region of eyes is extracted to yield binary images of the eye silhouettes in which the open and close stages of the eyes are revealed. The portion of the close states of the eyes during a certain number of frames is calculated to track the drowsiness signs. If this portion exceeds a predefined threshold, the system concludes that the driver tends to falling asleep and generate alert to the users.

Original language	English
Title of host publication	Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Viteman (BME6)
Editors	Toi Vo Van, Thanh An Nguyen Le, Nguyen Duc Thang
Publisher	Springer Singapore
Pages	723-727
Number of pages	5
ISBN (Electronic)	978-9811043611
ISBN (Print)	978-9811043604
DOIs	https://doi.org/10.1007/978-981-10-4361-1_124
Publication status	Early online - 24 Sept 2017
Event	BME International Conference on the Development of Biomedical Engineering in Vietnam - Ho Chi Minh, Vietnam Duration: 27 Jun 2017 → 29 Jun 2017

Publication series

Name	IFMBE Proceedings
Publisher	Springer
Volume	63
ISSN (Print)	1680-0737

Conference

Conference	BME International Conference on the Development of Biomedical Engineering in Vietnam
Country/Territory	Vietnam
City	Ho Chi Minh
Period	27/06/17 → 29/06/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

human-machine interface
drowsiness monitoring
face detection
decision tree

Access to Document

10.1007/978-981-10-4361-1_124

Drowsiness BME2016 in Vietnam
The final publication is available at Springer via http://dx.doi.org/10.1007/978-981-10-4361-1.
Accepted author manuscript (Post-print), 746 KB

Cite this

Nguyen, Q., Anh Tho, L. T., Van, T. V., Yu, H., & Thang, N. D. (2017). Visual based drowsiness detection using facial features. In T. Vo Van, T. A. N. Le, & N. D. Thang (Eds.), Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Viteman (BME6) (pp. 723-727). (IFMBE Proceedings; Vol. 63). Springer Singapore. Advance online publication. https://doi.org/10.1007/978-981-10-4361-1_124

@inproceedings{104b501a2f1a49e0a50a119c9b40a047,

title = "Visual based drowsiness detection using facial features",

abstract = "In this paper, a camera-based system is proposed to detect and monitor drowsiness of a car driver in real time. The system utilizes an RGB image to track the drivers{\textquoteright} face and their eyes to detect sleepy sign. For the face detection and segmentation, a robust method based on Haar features is applied. Within the segmented areas of faces, random forest is utilized to locate eye regions. Once the eyes are located, the local region of eyes is extracted to yield binary images of the eye silhouettes in which the open and close stages of the eyes are revealed. The portion of the close states of the eyes during a certain number of frames is calculated to track the drowsiness signs. If this portion exceeds a predefined threshold, the system concludes that the driver tends to falling asleep and generate alert to the users.",

keywords = "human-machine interface, drowsiness monitoring, face detection, decision tree",

author = "Quang Nguyen and \{Anh Tho\}, \{Le T.\} and Van, \{Toi Vo\} and Hui Yu and Thang, \{Nguyen Duc\}",

year = "2017",

month = sep,

day = "24",

doi = "10.1007/978-981-10-4361-1\_124",

language = "English",

isbn = "978-9811043604",

series = "IFMBE Proceedings",

publisher = "Springer Singapore",

pages = "723--727",

editor = "\{Vo Van\}, Toi and Le, \{Thanh An Nguyen\} and Thang, \{Nguyen Duc\}",

booktitle = "Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Viteman (BME6)",

address = "Singapore",

note = "BME International Conference on the Development of Biomedical Engineering in Vietnam ; Conference date: 27-06-2017 Through 29-06-2017",

}

Nguyen, Q, Anh Tho, LT, Van, TV, Yu, H & Thang, ND 2017, Visual based drowsiness detection using facial features. in T Vo Van, TAN Le & ND Thang (eds), Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Viteman (BME6). IFMBE Proceedings, vol. 63, Springer Singapore, pp. 723-727, BME International Conference on the Development of Biomedical Engineering in Vietnam, Ho Chi Minh, Vietnam, 27/06/17. https://doi.org/10.1007/978-981-10-4361-1_124

Visual based drowsiness detection using facial features. / Nguyen, Quang; Anh Tho, Le T.; Van, Toi Vo et al.
Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Viteman (BME6). ed. / Toi Vo Van; Thanh An Nguyen Le; Nguyen Duc Thang. Springer Singapore, 2017. p. 723-727 (IFMBE Proceedings; Vol. 63).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Visual based drowsiness detection using facial features

AU - Nguyen, Quang

AU - Anh Tho, Le T.

AU - Van, Toi Vo

AU - Yu, Hui

AU - Thang, Nguyen Duc

PY - 2017/9/24

Y1 - 2017/9/24

N2 - In this paper, a camera-based system is proposed to detect and monitor drowsiness of a car driver in real time. The system utilizes an RGB image to track the drivers’ face and their eyes to detect sleepy sign. For the face detection and segmentation, a robust method based on Haar features is applied. Within the segmented areas of faces, random forest is utilized to locate eye regions. Once the eyes are located, the local region of eyes is extracted to yield binary images of the eye silhouettes in which the open and close stages of the eyes are revealed. The portion of the close states of the eyes during a certain number of frames is calculated to track the drowsiness signs. If this portion exceeds a predefined threshold, the system concludes that the driver tends to falling asleep and generate alert to the users.

AB - In this paper, a camera-based system is proposed to detect and monitor drowsiness of a car driver in real time. The system utilizes an RGB image to track the drivers’ face and their eyes to detect sleepy sign. For the face detection and segmentation, a robust method based on Haar features is applied. Within the segmented areas of faces, random forest is utilized to locate eye regions. Once the eyes are located, the local region of eyes is extracted to yield binary images of the eye silhouettes in which the open and close stages of the eyes are revealed. The portion of the close states of the eyes during a certain number of frames is calculated to track the drowsiness signs. If this portion exceeds a predefined threshold, the system concludes that the driver tends to falling asleep and generate alert to the users.

KW - human-machine interface

KW - drowsiness monitoring

KW - face detection

KW - decision tree

U2 - 10.1007/978-981-10-4361-1_124

DO - 10.1007/978-981-10-4361-1_124

M3 - Conference contribution

SN - 978-9811043604

T3 - IFMBE Proceedings

SP - 723

EP - 727

BT - Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Viteman (BME6)

A2 - Vo Van, Toi

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A2 - Thang, Nguyen Duc

PB - Springer Singapore

T2 - BME International Conference on the Development of Biomedical Engineering in Vietnam

Y2 - 27 June 2017 through 29 June 2017

ER -

Visual based drowsiness detection using facial features

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Cite this