Abhinav Dhall
Roland Goecke
ABSTRACT
Emotion recognition is a very active field of research. The Emotion Recognition In The Wild Challenge and Workshop (EmotiW) 2013 Grand Challenge consists of an audio-video based emotion classification challenges, which mimics real-world conditions. Traditionally, emotion recognition has been performed on laboratory controlled data. While undoubtedly worthwhile at the time, such laboratory controlled data poorly represents the environment and conditions faced in real-world situations. The goal of this Grand Challenge is to define a common platform for evaluation of emotion recognition methods in real-world conditions. The database in the 2013 challenge is the Acted Facial Expression in the Wild (AFEW), which has been collected from movies showing close-to-real-world conditions.
- Patrick Lucey, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In CVPR4HB10, 2010.Google Scholar
- Maja Pantic, Michel François Valstar, Ron Rademaker, and Ludo Maat. Web-based database for facial expression analysis. In Proceedings of the IEEE International Conference on Multimedia and Expo, ICME'05, 2005.Google ScholarCross Ref
- Michel Valstar, Bihan Jiang, Marc Mehu, Maja Pantic, and Scherer Klaus. The first facial expression recognition and analysis challenge. In Proceedings of the Ninth IEEE International Conference on Automatic Face Gesture Recognition and Workshops, FG'11, pages 314--321, 2011.Google ScholarCross Ref
- Gary McKeown, Michel François Valstar, Roderick Cowie, and Maja Pantic. The semaine corpus of emotionally coloured character interactions. In IEEE ICME, 2010.Google ScholarCross Ref
- Björn Schuller, Michel François Valstar, Florian Eyben, Gary McKeown, Roddy Cowie, and Maja Pantic. Avec 2011-the first international audio/visual emotion challenge. In ACII (2), pages 415--424, 2011. Google ScholarDigital Library
- Björn Schuller, Michel Valstar, Florian Eyben, Roddy Cowie, and Maja Pantic. Avec 2012: the continuous audio/visual emotion challenge. In ICMI, pages 449--456, 2012. Google ScholarDigital Library
- Abhinav Dhall, Jyoti Joshi, Ibrahim Radwan, and Roland Goecke. Finding happiest moments in a social context. In ACCV, 2012. Google ScholarDigital Library
- Abhinav Dhall, Roland Goecke, Simon Lucey, and Tom Gedeon. A semi-automatic method for collecting richly labelled large facial expression databases from movies. IEEE Multimedia, 19(3):34--41, 2012. Google ScholarDigital Library
- Jacob Whitehill, Gwen Littlewort, Ian R. Fasel, Marian Stewart Bartlett, and Javier R. Movellan. Toward Practical Smile Detection. IEEE TPAMI, 2009. Google ScholarDigital Library
- Abhinav Dhall, Roland Goecke, Simon Lucey, and Tom Gedeon. Static Facial Expression Analysis In Tough Conditions: Data, Evaluation Protocol And Benchmark. In ICCVW, BEFIT'11, 2011.Google ScholarCross Ref
- P.F. Felzenszwalb and D.P. Huttenlocher. Pictorial Structures for Object Recognition. IJCV, 2005. Google ScholarDigital Library
- Xiangxin Zhu and Deva Ramanan. Face detection, pose estimation, and landmark localization in the wild. In CVPR, pages 2879--2886, 2012. Google ScholarDigital Library
- Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In CVPR, pages 886--893, 2005. Google ScholarDigital Library
- Tobias Gehrig and Hazım Kemal Ekenel. A common framework for real-time emotion recognition and facial action unit detection. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, pages 1--6. IEEE, 2011.Google ScholarCross Ref
- Guoying Zhao and Matti Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE TPAMI, 29:915--928, 2007. Google ScholarDigital Library
- Björn Schuller, Michel Valstar, Florian Eyben, Gary McKeown, Roddy Cowie, and Maja Pantic. Avec 2011--the first international audio/visual emotion challenge. In Affective Computing and Intelligent Interaction, pages 415--424. Springer Berlin Heidelberg, 2011. Google ScholarDigital Library
- Björn Schuller, Stefan Steidl, Anton Batliner, Felix Burkhardt, Laurence Devillers, Christian A Müller, and Shrikanth S Narayanan. The interspeech 2010 paralinguistic challenge. In INTERSPEECH, pages 2794--2797, 2010.Google Scholar
- Florian Eyben, Martin Wollmer, and Bjorn Schuller. Openear--introducing the munich open-source emotion and affect recognition toolkit. In Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on, pages 1--6. IEEE, 2009.Google ScholarCross Ref
- Florian Eyben, Martin Wöllmer, and Björn Schuller. Opensmile: the munich versatile and fast open-source audio feature extractor. In ACM Multimedia, pages 1459--1462, 2010. Google ScholarDigital Library
Index Terms
- Emotion recognition in the wild challenge 2013
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