Drowsiness Eye Detection using Convolutional Neural Network

Arwoko, Heru and Limanto, Susana and Asmawati, Endah (2023) Drowsiness Eye Detection using Convolutional Neural Network. In: Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023), 14-15 September 2023, Yogyakarta.

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Official URL / DOI: https://doi.org/10.2991/978-94-6463-288-0_54


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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Drowsiness; Eye Fatigue Detection; Convolutional Neural Network; Interpolation
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Engineering > Department of Informatic
Depositing User: Susana 6169
Date Deposited: 24 Nov 2023 01:42
Last Modified: 24 Nov 2023 01:42
URI: http://repository.ubaya.ac.id/id/eprint/45420

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