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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Volume 9 | Issue 5
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| Paper Title: | Driver drowsiness detection |
| Authors Name: | K.Nandini , M.chandana , G. Vandana sai , G.Lavanya , R. Snehanjali |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217906
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| Published Paper Id: | IJNRD2404305 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| 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. |
| Keywords: | Drowsiness Detection; facial expression; Machine learning; behavioral measures. |
| Cite Article: | "Driver drowsiness detection", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.d32-d40, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404305.pdf |
| Downloads: | 00030 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404305 Registration ID: 217906 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: d32-d40 Country: Anantapur, Andhra Pradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404305 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404305 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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