@inproceedings{67194300442e4b46a00d40cf42738df8,
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.",
keywords = "Affective computing, Audio-visual corpus, Challenge, Emotion, Features, Multimodal fusion",
author = "Ya Li and Jianhua Tao and Bj{\"o}rn Schuller and Shiguang Shan and Dongmei Jiang and Jia Jia",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2016.",
year = "2016",
doi = "10.1007/978-981-10-3005-5_55",
language = "English",
isbn = "9789811030048",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "667--678",
editor = "Tieniu Tan and Xilin Chen and Xuelong Li and Jian Yang and Hong Cheng and Jie Zhou",
booktitle = "Pattern Recognition - 7th Chinese Conference, CCPR 2016, Proceedings",
}