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Journal : Best : Journal of Applied Electrical, Science and Technology

DROWSY DETECTION FROM VIDEO DRIVER FACE BASED ON EYE AND MOUTH FEATURES EXTRACTION USING THE CONVOLUTION NEURAL NETWORK METHOD Akhmad Solikin; Endang Setyati
BEST Vol 2 No 1 (2020): BEST
Publisher : Program Studi Teknik Elektro Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/best.vol2.no1.2584

Abstract

This research was conducted in an effort to minimize the occurrence of road traffic accidents. In this study detected the level of fatigue and sleepiness from the driver's face video based on the extraction of eye and mouth features using the CNN method. The dataset in this study is 300 data with 3 different classes namely drowsiness 100 data, sleepy 100 data and normal 100 data. The number of epochs used in research to achieve high accuracy is as much as 50. In the test results it is known that the validation of accuracy has increased in each of the input layer results.