TY - JOUR
T1 - Analyzing group-level emotion with global alignment kernel based approach
AU - Huang, Xiaohua
AU - Dhall, Abhinav
AU - Goecke, Roland
AU - Pietikainen, Matti K.
AU - Zhao, Guoying
PY - 2022/4
Y1 - 2022/4
N2 - From the perspective of social science, understanding group emotion has become increasingly important for teams to considerably accomplish organizational work. Currently, automatically analyzing the perceived affect of a group of people has been received increasingly interest in affective computing community. The variability in group size makes difficulty for group-level emotion recognition to straightforwardly measure the feature distance of two group-level images. To alleviate this problem, this paper aims to design a new method to effectively analyze the group behavior from a group-level image. Motivated by time-series kernel approaches explored in dynamic facial expression classification, this paper mainly concentrates on global alignment kernel and design support vector machine with the combined global alignment kernels (SVM-CGAK) to better recognize group-level emotion. Intensive experiments are conducted on three challenging group-level emotion databases. The experimental results demonstrate that the proposed approach achieves promising performance for group-level emotion recognition compared with the recent state-of-the-art methods.
AB - From the perspective of social science, understanding group emotion has become increasingly important for teams to considerably accomplish organizational work. Currently, automatically analyzing the perceived affect of a group of people has been received increasingly interest in affective computing community. The variability in group size makes difficulty for group-level emotion recognition to straightforwardly measure the feature distance of two group-level images. To alleviate this problem, this paper aims to design a new method to effectively analyze the group behavior from a group-level image. Motivated by time-series kernel approaches explored in dynamic facial expression classification, this paper mainly concentrates on global alignment kernel and design support vector machine with the combined global alignment kernels (SVM-CGAK) to better recognize group-level emotion. Intensive experiments are conducted on three challenging group-level emotion databases. The experimental results demonstrate that the proposed approach achieves promising performance for group-level emotion recognition compared with the recent state-of-the-art methods.
KW - Convolution neural network
KW - Facial expression analysis
KW - Global alignment kernels
KW - Group-level emotion recognition
KW - Multiple kernel learning
UR - http://www.scopus.com/inward/record.url?scp=85075397895&partnerID=8YFLogxK
U2 - 10.1109/TAFFC.2019.2953664
DO - 10.1109/TAFFC.2019.2953664
M3 - Article
AN - SCOPUS:85075397895
VL - 13
SP - 713
EP - 728
JO - IEEE Transactions on Affective Computing
JF - IEEE Transactions on Affective Computing
SN - 1949-3045
IS - 2
ER -