University of Oulu

X. Huang, A. Dhall, R. Goecke, M. K. Pietikainen and G. Zhao, "Analyzing group-level emotion with global alignment kernel based approach," in IEEE Transactions on Affective Computing. doi: 10.1109/TAFFC.2019.2953664

Analyzing group-level emotion with global alignment kernel based approach

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Author: Huang, Xiaohua1,2; Dhall, Abhinav3; Goecke, Roland4;
Organizations: 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
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 22.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019120946268
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-12-09
Description:

Abstract

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.

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Series: IEEE transactions on affective computing
ISSN: 2371-9850
ISSN-E: 1949-3045
ISSN-L: 2371-9850
Volume: Early access
Issue: Early access
Pages: 1 - 16
DOI: 10.1109/TAFFC.2019.2953664
OADOI: https://oadoi.org/10.1109/TAFFC.2019.2953664
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
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