Driver drowsiness detection using multi-channel second order blind identifications

Zhang, C; Wu, X; Zheng, X; Yu, S

Driver drowsiness detection using multi-channel second order blind identifications

Zhang, C Wu, X Zheng, X Yu, S

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 11829 - 11843
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Zhang, C	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zheng, X	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 11829 - 11843	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132682
dc.description.abstract	© 2019 IEEE. It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180102828
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2891971	en_US
dc.title	Driver drowsiness detection using multi-channel second order blind identifications	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2019 IEEE. It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.

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http://hdl.handle.net/10453/132682