DROWSY DETECTION FROM VIDEO DRIVER FACE BASED ON EYE AND MOUTH FEATURES EXTRACTION USING THE CONVOLUTION NEURAL NETWORK METHOD

Solikin, Akhmad
Setyati, Endang

Best : Journal of Applied Electrical, Science and Technology
Vol 2 No 1 (2020): BEST

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.