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Erschienen in:
Saliency Region Detection via Graph Model and Statistical Learning Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

MEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016

verfasst von : Ya Li, Jianhua Tao, Björn Schuller, Shiguang Shan, Dongmei Jiang, Jia Jia

Erschienen in: Pattern Recognition

Verlag: Springer Singapore

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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.

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Vorheriges Kapitel The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge
Nächstes Kapitel Video Based Emotion Recognition Using CNN and BRNN
Fußnoten
Literatur
1.
McKeown, G., Valstar, M., Cowie, R., Pantic, M., Schröder, M.: The semaine database: annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Trans. Affect. Comput. 3, 5–17 (2012)CrossRef
2.
Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia 19, 34–41 (2012)CrossRef
3.
Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark. In: IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, pp. 2106–2112, November 2011
4.
Schuller, B., Valstar, M., Eyben, F., McKeown, G., Cowie, R., Pantic, M.: AVEC 2011-the first international audio/visual emotion challenge. In: Affective Computing and Intelligent Interaction, pp. 415–424 (2011)
5.
Schuller, B., Steidl, S., Batliner, A.: The interspeech 2009 emotion challenge. In: Interspeech, pp. 312–315 (2009)
6.
Valstar, M.F., Jiang, B., Mehu, M., Pantic, M., Scherer, K.: The first facial expression recognition and analysis challenge. In: 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011), pp. 921–926 (2011)
7.
Dhall, A., Ramana Murthy, O., Goecke, R., Joshi, J., Gedeon, T.: Video and image based emotion recognition challenges in the wild: Emotiw 2015. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 423–426 (2015)
8.
Ringeval, F., Schuller, B., Valstar, M., Jaiswal, S., Marchi, E., Lalanne, D., et al.: AV+EC 2015: the first affect recognition challenge bridging across audio, video, and physiological data. In: Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge, pp. 3–8 (2015)
9.
Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. Speech Commun. 48, 1162–1181 (2006)CrossRef
10.
Wu, C.-H., Lin, J.-C., Wei, W.-L.: Survey on audiovisual emotion recognition: databases, features, and data fusion strategies. APSIPA Trans. Signal Inf. Process. 3, 12 (2014)CrossRef
11.
Douglas-Cowie, E., Campbell, N., Cowie, R., Roach, P.: Emotional speech: towards a new generation of databases. Speech Commun. 40, 33–60 (2003)CrossRefMATH
12.
Grimm, M., Kroschel, K., Narayanan, S.: The Vera am Mittag German audio-visual emotional speech database. In: International Conference on Multimedia Computing and Systems/International Conference on Multimedia and Expo, pp. 865–868 (2008)
13.
Devillers, L., Cowie, R., Martin, J.C., Douglas-Cowie, E., Abrilian, S., Mcrorie, M.: Real life emotions in French and English TV video clips: an integrated annotation protocol combining continuous and discrete approaches. In: International Conference on Language Resources and Evaluation, pp. 1105–1110 (2006)
14.
Clavel, C., Vasilescu, I., Devillers, L., Richard, G., Ehrette, T., Sedogbo, C.: The SAFE corpus: illustrating extreme emotions in dynamic situations. In: First International Workshop on Emotion: Corpora for Research on Emotion and Affect, pp. 76–79 (2006)
15.
Bao, W., Li, Y., Gu, M., Yang, M., Li, H., Chao, L., et al.: Building a Chinese natural emotional audio-visual database. In: 2014 International Conference on Signal Processing, pp. 583–587 (2014)
16.
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., et al.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18, 32–80 (2001)CrossRef
17.
Gobl, C., Chasaide, A.N.: The role of voice quality in communicating emotion, mood and attitude. Speech Commun. 40, 189–212 (2003)CrossRefMATH
18.
Eyben, F., Weninger, F., Gross, F., Schuller, B.: Recent developments in openSMILE, the Munich open-source multimedia feature extractor. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 835–838 (2013)
19.
Xiong, X., Torre, F.D.L.: Supervised descent method and its applications to face alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 532–539 (2013)
20.
Viola, P., Jones, M.J.: Robust real-time object detection. Int. J. Comput. Vision 57, 87 (2001)
21.
Zhao, G., Pietikinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 915–928 (2007)CrossRef
Metadaten
Titel
MEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016
verfasst von
Ya Li
Jianhua Tao
Björn Schuller
Shiguang Shan
Dongmei Jiang
Jia Jia
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Pattern Recognition

Print ISBN: 978-981-10-3004-8

Electronic ISBN: 978-981-10-3005-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-3005-5_55

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