Driver Identification and Detection of Drowsiness while Driving
This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/6/2603 |
| _version_ | 1797242161692934144 |
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| author | Sonia Díaz-Santos Óscar Cigala-Álvarez Ester Gonzalez-Sosa Pino Caballero-Gil Cándido Caballero-Gil |
| author_facet | Sonia Díaz-Santos Óscar Cigala-Álvarez Ester Gonzalez-Sosa Pino Caballero-Gil Cándido Caballero-Gil |
| author_sort | Sonia Díaz-Santos |
| collection | DOAJ |
| description | This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems. |
| first_indexed | 2024-04-24T18:34:49Z |
| format | Article |
| id | doaj.art-c38b6bc80de94221a870c84a1b7a22aa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:34:49Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c38b6bc80de94221a870c84a1b7a22aa2024-03-27T13:20:13ZengMDPI AGApplied Sciences2076-34172024-03-01146260310.3390/app14062603Driver Identification and Detection of Drowsiness while DrivingSonia Díaz-Santos0Óscar Cigala-Álvarez1Ester Gonzalez-Sosa2Pino Caballero-Gil3Cándido Caballero-Gil4Department of Computer Engineering and Systems, University of La Laguna, 38271 Tenerife, SpainDepartment of Computer Engineering and Systems, University of La Laguna, 38271 Tenerife, SpaineXtended Reality Lab, Nokia, 28045 Madrid, SpainDepartment of Computer Engineering and Systems, University of La Laguna, 38271 Tenerife, SpainDepartment of Computer Engineering and Systems, University of La Laguna, 38271 Tenerife, SpainThis paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems.https://www.mdpi.com/2076-3417/14/6/2603facial recognitiondrowsiness detectiondriver safetymachine learningvideo-based identification |
| spellingShingle | Sonia Díaz-Santos Óscar Cigala-Álvarez Ester Gonzalez-Sosa Pino Caballero-Gil Cándido Caballero-Gil Driver Identification and Detection of Drowsiness while Driving Applied Sciences facial recognition drowsiness detection driver safety machine learning video-based identification |
| title | Driver Identification and Detection of Drowsiness while Driving |
| title_full | Driver Identification and Detection of Drowsiness while Driving |
| title_fullStr | Driver Identification and Detection of Drowsiness while Driving |
| title_full_unstemmed | Driver Identification and Detection of Drowsiness while Driving |
| title_short | Driver Identification and Detection of Drowsiness while Driving |
| title_sort | driver identification and detection of drowsiness while driving |
| topic | facial recognition drowsiness detection driver safety machine learning video-based identification |
| url | https://www.mdpi.com/2076-3417/14/6/2603 |
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