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Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

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dc.contributor.author Ngxande, Mkhuseli
dc.contributor.author Tapamo, J-R
dc.contributor.author Burke, Michael G
dc.date.accessioned 2018-02-06T11:02:32Z
dc.date.available 2018-02-06T11:02:32Z
dc.date.issued 2017-11
dc.identifier.citation Ngxande, M., Tapamo, J-R. and Burke, M.G. 2017. Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques. 2017 PRASA-RobMech International Conference incorporating The 28th Annual Symposium of the Pattern Recognition Association of South Africa and The 10th Robotics and Mechatronics Conference of South Africa, 29 November - 1 December 2017, Central University of Technology Free State, Bloemfontein, South Africa, pp. 156-161 en_US
dc.identifier.isbn 978-1-5386-2313-8
dc.identifier.uri http://ieeexplore.ieee.org/document/8261140/
dc.identifier.uri DOI: 10.1109/RoboMech.2017.8261140
dc.identifier.uri http://hdl.handle.net/10204/10018
dc.description Copyright: 2017 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published paper. For access to the published version, please consult the publisher's website. en_US
dc.description.abstract This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features that can be extracted from the face to infer the level of drowsiness. These include eye blinks, head movements and yawning. However, the development of a drowsiness detection system that yields reliable and accurate results is a challenging task as it requires accurate and robust algorithms. A wide range of techniques has been examined to detect driver drowsiness in the past. The recent rise of deep learning requires that these algorithms be revisited to evaluate their accuracy in detection of drowsiness. As a result, this paper reviews machine learning techniques which include support vector machines, convolutional neural networks and hidden Markov models in the context of drowsiness detection. Furthermore, a meta-analysis is conducted on 25 papers that use machine learning techniques for drowsiness detection. The analysis reveals that support vector machine technique is the most commonly used technique to detect drowsiness, but convolutional neural networks performed better than the other two techniques. Finally, this paper lists publicly available datasets that can be used as benchmarks for drowsiness detection. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;20148
dc.subject Drowsiness detection en_US
dc.subject Facial expression en_US
dc.subject Machine learning en_US
dc.subject Behavioral measures en_US
dc.title Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Ngxande, M., Tapamo, J., & Burke, M. G. (2017). Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques. IEEE. http://hdl.handle.net/10204/10018 en_ZA
dc.identifier.chicagocitation Ngxande, Mkhuseli, J-R Tapamo, and Michael G Burke. "Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques." (2017): http://hdl.handle.net/10204/10018 en_ZA
dc.identifier.vancouvercitation Ngxande M, Tapamo J, Burke MG, Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques; IEEE; 2017. http://hdl.handle.net/10204/10018 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Ngxande, Mkhuseli AU - Tapamo, J-R AU - Burke, Michael G AB - This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features that can be extracted from the face to infer the level of drowsiness. These include eye blinks, head movements and yawning. However, the development of a drowsiness detection system that yields reliable and accurate results is a challenging task as it requires accurate and robust algorithms. A wide range of techniques has been examined to detect driver drowsiness in the past. The recent rise of deep learning requires that these algorithms be revisited to evaluate their accuracy in detection of drowsiness. As a result, this paper reviews machine learning techniques which include support vector machines, convolutional neural networks and hidden Markov models in the context of drowsiness detection. Furthermore, a meta-analysis is conducted on 25 papers that use machine learning techniques for drowsiness detection. The analysis reveals that support vector machine technique is the most commonly used technique to detect drowsiness, but convolutional neural networks performed better than the other two techniques. Finally, this paper lists publicly available datasets that can be used as benchmarks for drowsiness detection. DA - 2017-11 DB - ResearchSpace DP - CSIR KW - Drowsiness detection KW - Facial expression KW - Machine learning KW - Behavioral measures LK - https://researchspace.csir.co.za PY - 2017 SM - 978-1-5386-2313-8 T1 - Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques TI - Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques UR - http://hdl.handle.net/10204/10018 ER - en_ZA


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