TY - GEN
T1 - Driver Fatigue Detection Based on Face Mesh Features Using Deep Learning
AU - Nuralif, Imam
AU - Yuniarno, Eko Mulyanto
AU - Suprapto, Yoyon Kusnendar
AU - Wicaksono, Alif Aditya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The number vehicles and road users increases every year, this also has the potential to increase the risk of traffic accidents. Fatigue is the most dominant cause of accidents compared to several other factors. This research focuses on detecting driver fatigue. We use the mediapipe face mesh model to extract the key points on the face, next is to utilize the deep learning model, Long Short Term Memory (LSTM), which has been trained previously and implemented into mediapipe to detect driver fatigue. The data that is trained is the data point movement of facial features, so that the system can not only process one frame but several frames. The data given by the camera will be processed using the LSTM model's ability to detect long-term information, dynamically process data, and handle picture data using mediapipe in order to achieve low computational and high accuracy. The LSTM model has better accuracy in predicting facial features than the conventional random forest model.
AB - The number vehicles and road users increases every year, this also has the potential to increase the risk of traffic accidents. Fatigue is the most dominant cause of accidents compared to several other factors. This research focuses on detecting driver fatigue. We use the mediapipe face mesh model to extract the key points on the face, next is to utilize the deep learning model, Long Short Term Memory (LSTM), which has been trained previously and implemented into mediapipe to detect driver fatigue. The data that is trained is the data point movement of facial features, so that the system can not only process one frame but several frames. The data given by the camera will be processed using the LSTM model's ability to detect long-term information, dynamically process data, and handle picture data using mediapipe in order to achieve low computational and high accuracy. The LSTM model has better accuracy in predicting facial features than the conventional random forest model.
KW - Deep Learning
KW - Face Mesh
KW - Fatigue detection
KW - Mediapipe
UR - http://www.scopus.com/inward/record.url?scp=85171134866&partnerID=8YFLogxK
U2 - 10.1109/ISITIA59021.2023.10221053
DO - 10.1109/ISITIA59021.2023.10221053
M3 - Conference contribution
AN - SCOPUS:85171134866
T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
SP - 1
EP - 5
BT - 2023 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Y2 - 26 July 2023 through 27 July 2023
ER -