Drowsiness Eye Detection using Convolutional Neural Network

DOI

10.2991/978-94-6463-288-0_54How to use a DOI?

Keywords

Drowsiness; Eye Fatigue Detection; Convolutional Neural Network; Interpolation

Abstract

Eye fatigue while driving can cause drivers to be drowsy and less alert, which can potentially increase the risk of an accident. Existing data shows that the number of accidents in the world is increasing from year to year. One of the most common causes of accidents is fatigue and the leading cause of death is car accidents. Therefore, efforts are needed to reduce accidents due to fatigue. To overcome this, in this study, a system was developed to detect driver eye fatigue using the Convolutional Neural Network method with varying image sizes as input. The dataset consists of 1289 facial images that contain the eyes and is divided into 614 drowsiness eyes and 675 non-drowsiness eyes. In dealing with variations in image size, scaling was carried out using five interpolation methods, namely nearest-neighbor, bilinear, bicubic, inter-area, and lanczos4. The performance of the sleepy eye detection model will be evaluated based on accuracy and processing time. The results show that the image size of 64 × 64  with bilinear interpolation and 96 × 96 with inter-area interpolation gives the highest accuracy of 99%. Based on processing time, resizing the image to 8 × 8 size by using bilinear, bicubic, inter-area, and lanczos4 interpolation, results in the fastest processing time and high accuracy of 94% - 95%. The difference in accuracy with other image sizes is only 5%, with processing time for other size images up to 200 times longer than processing time for 8 × 8 image sizes.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Heru Arwoko
AU  - Susana Limanto
AU  - Endah Asmawati
PY  - 2023
DA  - 2023/11/19
TI  - Drowsiness Eye Detection using Convolutional Neural Network
BT  - Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
PB  - Atlantis Press
SP  - 650
EP  - 660
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-288-0_54
DO  - 10.2991/978-94-6463-288-0_54
ID  - Arwoko2023
ER  -