X. Huang, A. Dhall, R. Goecke, M. Pietikäinen and G. Zhao, "Analyzing Group-Level Emotion with Global Alignment Kernel based Approach," in IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 713-728, 1 April-June 2022, doi: 10.1109/TAFFC.2019.2953664.
Analyzing group-level emotion with global alignment kernel based approach
|Author:||Huang, Xiaohua1,2; Dhall, Abhinav3; Goecke, Roland4;|
1School of Computer Engineering, Nanjing Institute of Technology, China
2University of Oulu, Finland
3Human-Centred Artificial Intelligence, Monash University, Australia
4Human-Centred Technology Research Centre, University of Canberra, Australia
5Center for Machine Vision and Signal Analysis, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 22.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019120946268
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-12-09
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.
IEEE transactions on affective computing
|Pages:||713 - 728|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
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