Contactless Privacy-Preserving Head Movement Recognition Using Deep Learning for Driver Fatigue Detection

Hameed, H., Lubna, ., Usman, M., Abbas, H. , Tahir, A., Arshad, K., Assaleh, K., Alkhayyat, A., Imran, M. A. and Abbasi, Q. H. (2023) Contactless Privacy-Preserving Head Movement Recognition Using Deep Learning for Driver Fatigue Detection. In: 10th International Symposium on Networks, Computers and Communications (ISNCC'23), Doha, Qatar, 23-26 October 2023, ISBN 9798350335590 (doi: 10.1109/ISNCC58260.2023.10323825)

Abstract

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.

• Item Type: Conference Proceedings
• Additional Information: Partial support for this work was received from the Engineering and Physical Sciences Research Council (EPSRC) under the grant COG-MHEAR (ref. EP/T021063/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tahir, Dr Ahsen and Imran, Professor Muhammad and Usman, Dr Muhammad and Abbas, Dr Hasan and Abbasi, Professor Qammer and Hameed, Hira
• Authors: Hameed, H., Lubna, ., Usman, M., Abbas, H., Tahir, A., Arshad, K., Assaleh, K., Alkhayyat, A., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISSN: 2768-0940
• ISBN: 9798350335590

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