TY - GEN
T1 - Face Expression Recognition with Local Ternary Pattern Images using Convolutional Neural Network and Extreme Learning Machine
AU - Krisnahati, Ice
AU - Suciati, Nanik
AU - Chusnul Hidayati, Shintami
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Facial Expression Recognition (FER) performed computationally is an exciting task to explore in computer vision. Some methods have been proposed to handle variants of illumination in FER. Based on research, Local Ternary Pattern (LTP) as a feature extractor can handle the variation of lights. However, LTP is a traditional feature extractor that needs to be processed manually. Unlike LTP, Convolution Neural Network (CNN) architecture has an automatic feature extractor. Therefore, this study proposes LTP images as input into CNN architecture to handle light variations and keep the feature extraction automatically. Afterward, in the classification layer, Extreme Learning Machine (ELM) is employed as a classifier to improve the training speed of the original CNN classifier. The proposed model performance for the KDEF dataset with 10-fold cross-validation yields an accuracy of 85.51%
AB - Facial Expression Recognition (FER) performed computationally is an exciting task to explore in computer vision. Some methods have been proposed to handle variants of illumination in FER. Based on research, Local Ternary Pattern (LTP) as a feature extractor can handle the variation of lights. However, LTP is a traditional feature extractor that needs to be processed manually. Unlike LTP, Convolution Neural Network (CNN) architecture has an automatic feature extractor. Therefore, this study proposes LTP images as input into CNN architecture to handle light variations and keep the feature extraction automatically. Afterward, in the classification layer, Extreme Learning Machine (ELM) is employed as a classifier to improve the training speed of the original CNN classifier. The proposed model performance for the KDEF dataset with 10-fold cross-validation yields an accuracy of 85.51%
KW - Convolutional Neural Network
KW - Extreme Learning Machine
KW - Facial Expression Recognition
KW - KDEF
KW - Local Ternary Pattern
UR - http://www.scopus.com/inward/record.url?scp=85150456934&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE57756.2022.10057710
DO - 10.1109/ICITISEE57756.2022.10057710
M3 - Conference contribution
AN - SCOPUS:85150456934
T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
SP - 46
EP - 50
BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Y2 - 13 December 2022 through 14 December 2022
ER -
