Paper Info
Title
MEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016.
Abstract
Emotion recognition is a significant research filed of pattern recognition and artificial intelligence. The Multimodal Emotion Recognition Challenge (MEC) is a part of the 2016 Chinese Conference on Pattern Recognition (CCPR). The goal of this competition is to compare multimedia processing and machine learning methods for multimodal emotion recognition. The challenge also aims to provide a common benchmark data set, to bring together the audio and video emotion recognition communities, and to promote the research in multimodal emotion recognition. The data used in this challenge is the Chinese Natural Audio-Visual Emotion Database (CHEAVD), which is selected from Chinese movies and TV programs. The discrete emotion labels are annotated by four experienced assistants. Three sub-challenges are defined: audio, video and multimodal emotion recognition. This paper introduces the baseline audio, visual features, and the recognition results by Random Forests.
Year
DOI
Venue
2016
10.1007/978-981-10-3005-5_55
Communications in Computer and Information Science
Keywords
Field
DocType
Audio-visual corpus,Features,Multimodal fusion,Challenge,Emotion,Affective computing
Computer science,Emotion recognition,Speech recognition,Affective computing,Random forest
Conference
Volume
ISSN
Citations 
663
1865-0929
10
PageRank 
References 
Authors
0.51
15
6
Name
Order
Citations
PageRank
Ya Li13611.21
Jianhua Tao2848138.00
Björn Schuller36749463.50
Shiguang Shan46322283.75
Jiang Dongmei511515.28
Jia Jia645155.08