TY - GEN
T1 - Emotion Recognition on Partial Faces with Histogram of Oriented Gradients and Local Binary Patterns
AU - Kaewnoparat, Natagorn
AU - Phienthrakul, Tanasanee
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Facial expression is a way to improve the ability of communication. If machines can understand the emotion, their responses should be improved. Many researchers can teach the machine to learn emotions from facial images. Sometimes, the face in an image may not be complete, especially when the masks are used in pandemic. Some parts of the human face cannot be reached. However, if the important features can be extracted from the remaining parts, the machine should be able to learn. In this paper, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are considered to extract features from the upper-frontal facial images. The concatenation of these features is also studied. The experimental results show the combined features provide the most stability performance with the highest averaged accuracy at 68.39% on Support Vector Machine.
AB - Facial expression is a way to improve the ability of communication. If machines can understand the emotion, their responses should be improved. Many researchers can teach the machine to learn emotions from facial images. Sometimes, the face in an image may not be complete, especially when the masks are used in pandemic. Some parts of the human face cannot be reached. However, if the important features can be extracted from the remaining parts, the machine should be able to learn. In this paper, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are considered to extract features from the upper-frontal facial images. The concatenation of these features is also studied. The experimental results show the combined features provide the most stability performance with the highest averaged accuracy at 68.39% on Support Vector Machine.
KW - Feature Extraction
KW - Histogram of Oriented Gradient
KW - Local Binary Pattern
KW - Partial Face Recognition
UR - https://www.scopus.com/pages/publications/85170044929
U2 - 10.1109/ISIEA58478.2023.10212271
DO - 10.1109/ISIEA58478.2023.10212271
M3 - Conference contribution
AN - SCOPUS:85170044929
T3 - 2023 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2023
BT - 2023 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2023
Y2 - 15 July 2023 through 16 July 2023
ER -