TY - GEN
T1 - Performance Study of Facial Expression Recognition Using Convolutional Neural Network
AU - Aza, Marde Fasma ul
AU - Suciati, Nanik
AU - Hidayati, Shintami Chusnul
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - Facial expression depicts human emotions. Recognition of facial expression is used in various fields, such as for a better understanding of the customer's desires during a home design consultation and to find out the pain suffered by a patient during medical treatment. This research explores deep learning techniques based on Convolutional Neural Network (CNN) on facial expression recognition. The three pre-trained CNN models, namely VGG16, Resnet50, and Senet50, are retrained using different learning rate values and optimization functions. Trials on The Extended Cohn-Kanade Dataset (CK +) consisting of 7 expression classes, namely anger, neutral, disgust, fear, joy, sadness, and surprise, produce the best accuracy of 97% obtained by the VGG16 architecture with Adam's optimization function and learning rate of 0.001.
AB - Facial expression depicts human emotions. Recognition of facial expression is used in various fields, such as for a better understanding of the customer's desires during a home design consultation and to find out the pain suffered by a patient during medical treatment. This research explores deep learning techniques based on Convolutional Neural Network (CNN) on facial expression recognition. The three pre-trained CNN models, namely VGG16, Resnet50, and Senet50, are retrained using different learning rate values and optimization functions. Trials on The Extended Cohn-Kanade Dataset (CK +) consisting of 7 expression classes, namely anger, neutral, disgust, fear, joy, sadness, and surprise, produce the best accuracy of 97% obtained by the VGG16 architecture with Adam's optimization function and learning rate of 0.001.
KW - CNN
KW - Facial Expression Recognition
KW - Resnet50
KW - Senet50
KW - VGG16
UR - http://www.scopus.com/inward/record.url?scp=85104496537&partnerID=8YFLogxK
U2 - 10.1109/ICSITech49800.2020.9392070
DO - 10.1109/ICSITech49800.2020.9392070
M3 - Conference contribution
AN - SCOPUS:85104496537
T3 - 2020 6th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020
SP - 121
EP - 126
BT - 2020 6th International Conference on Science in Information Technology
A2 - Kasim, Anita Ahmad
A2 - Pranolo, Andri
A2 - Hernandez, Leonel
A2 - Wibawa, Aji Prasetya
A2 - Voliansky, Roman
A2 - Ngemba, Hajra Rasmita
A2 - Drezewski, Rafal
A2 - Zachir, Zachir
A2 - Haviluddin, Haviluddin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Science in Information Technology, ICSITech 2020
Y2 - 21 October 2020 through 22 October 2020
ER -