ABSTRACT
The Second Emotion Recognition In The Wild Challenge (EmotiW) 2014 consists of an audio-video based emotion classification challenge, which mimics the real-world conditions. Traditionally, emotion recognition has been performed on data captured in constrained lab-controlled like environment. While this data was a good starting point, such lab controlled data poorly represents the environment and conditions faced in real-world situations. With the exponential increase in the number of video clips being uploaded online, it is worthwhile to explore the performance of emotion recognition methods that work `in the wild'. The goal of this Grand Challenge is to carry forward the common platform defined during EmotiW 2013, for evaluation of emotion recognition methods in real-world conditions. The database in the 2014 challenge is the Acted Facial Expression In Wild (AFEW) 4.0, which has been collected from movies showing close-to-real-world conditions. The paper describes the data partitions, the baseline method and the experimental protocol.
- M. S. Bartlett, G. Littlewort, M. G. Frank, C. Lainscsek, I. R. Fasel, and J. R. Movellan. Automatic recognition of facial actions in spontaneous expressions. Journal of Multimedia, 1(6):22--35, 2006.Google ScholarCross Ref
- A. Dhall, R. Goecke, J. Joshi, M. Wagner, and T. Gedeon. Emotion recognition in the wild challenge 2013. In Proceedings of the ACM on International Conference on Multimodal Interaction (ICMI), pages 509--516, 2013. Google ScholarDigital Library
- A. Dhall, R. Goecke, S. Lucey, and T. Gedeon. Acted Facial Expressions in the Wild Database. In Technical Report, 2011.Google Scholar
- A. Dhall, R. Goecke, S. Lucey, and T. Gedeon. Static Facial Expression Analysis In Tough Conditions: Data, Evaluation Protocol And Benchmark. In Proceedings of the IEEE International Conference on Computer Vision and Workshops BEFIT, pages 2106--2112, 2011.Google Scholar
- A. Dhall, R. Goecke, S. Lucey, and T. Gedeon. Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia, 19(3):0034, 2012. Google ScholarDigital Library
- A. Dhall, J. Joshi, I. Radwan, and R. Goecke. Finding Happiest Moments in a Social Context. In Proceedings of the Asian Conference on Computer Vision (ACCV), pages 613--626, 2012. Google ScholarDigital Library
- A. Dhall, K. Sikka, G. Littlewort, R. Goecke, and M. Bartlett. A Discriminative Parts Based Model Approach for Fiducial Points Free and Shape Constrained Head Pose Normalisation In The Wild. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pages 1--8, 2014.Google ScholarCross Ref
- F. Eyben, M. Wollmer, and B. Schuller. Openear-introducing the munich open-source emotion and affect recognition toolkit. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction (ACII), pages 1--6, 2009.Google ScholarCross Ref
- F. Eyben, M. Wöllmer, and B. Schuller. Opensmile: the munich versatile and fast open-source audio feature extractor. In Proceedings of the ACM International Conference on Multimedia (MM), pages 1459--1462, 2010. Google ScholarDigital Library
- P. F. Felzenszwalb and D. P. Huttenlocher. Pictorial Structures for Object Recognition. International Journal on Computer Vision, 61(1):55--79, 2005. Google ScholarDigital Library
- R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker. Multi-PIE. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG), pages 1--8, 2008.Google Scholar
- S. E. Kahou, C. Pal, X. Bouthillier, P. Froumenty, S. Jean, K. R. Konda, P. Vincent, A. Courville, and Y. Bengio. Combining modality specific deep neural networks for emotion recognition in video. In Proceedings of the ACM on International Conference on Multimodal Interaction (ICMI), pages 543--550, 2013. Google ScholarDigital Library
- T. Kanade, J. F. Cohn, and Y. Tian. Comprehensive database for facial expression analysis. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG), pages 46--53, 2000. Google ScholarDigital Library
- M. Liu, R. Wang, Z. Huang, S. Shan, and X. Chen. Partial least squares regression on grassmannian manifold for emotion recognition. In Proceedings of the ACM on International Conference on Multimodal Interaction (ICMI), pages 525--530, 2013. Google ScholarDigital Library
- S. Nowee. Facial Expression Recognition in the Wild: The Influence of Temporal Information. PhD thesis, University of Amsterdam, 2014.Google Scholar
- B. Schuller, S. Steidl, A. Batliner, F. Burkhardt, L. Devillers, C. A. Müller, and S. S. Narayanan. The interspeech 2010 paralinguistic challenge. In INTERSPEECH, pages 2794--2797, 2010.Google Scholar
- B. Schuller, M. Valstar, F. Eyben, G. McKeown, R. Cowie, and M. Pantic. Avec 2011--the first international audio/visual emotion challenge. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction (ACII), pages 415--424, 2011. Google ScholarDigital Library
- K. Sikka, A. Dhall, and M. Bartlett. Weakly supervised pain localization using multiple instance learning. Proceedings of the IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pages 1--8, 2013.Google ScholarCross Ref
- K. Sikka, K. Dykstra, S. Sathyanarayana, G. Littlewort, and M. Bartlett. Multiple kernel learning for emotion recognition in the wild. In Proceedings of the ACM on International Conference on Multimodal Interaction (ICMI), pages 517--524, 2013. Google ScholarDigital Library
- F. Wallhoff. Facial expressions and emotion database, 2006. http://www.mmk.ei.tum.de/ waf/fgnet/feedtum.html.Google Scholar
- J. Whitehill, G. Littlewort, I. R. Fasel, M. S. Bartlett, and J. R. Movellan. Toward Practical Smile Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence, 31(11):2106--2111, 2009. Google ScholarDigital Library
- X. Xiong and F. De la Torre. Supervised descent method and its applications to face alignment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 532--539, 2013. Google ScholarDigital Library
- G. Zhao and M. Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transaction on Pattern Analysis and Machine Intelligence, 29(6):915--928, 2007. Google ScholarDigital Library
- X. Zhu and D. Ramanan. Face detection, pose estimation, and landmark localization in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2879--2886, 2012. Google ScholarDigital Library
Index Terms
- Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol
Recommendations
EmotiW 2016: video and group-level emotion recognition challenges
ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal InteractionThis paper discusses the baseline for the Emotion Recognition in the Wild (EmotiW) 2016 challenge. Continuing on the theme of automatic affect recognition `in the wild', the EmotiW challenge 2016 consists of two sub-challenges: an audio-video based ...
Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015
ICMI '15: Proceedings of the 2015 ACM on International Conference on Multimodal InteractionThe third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on ...
From individual to group-level emotion recognition: EmotiW 5.0
ICMI '17: Proceedings of the 19th ACM International Conference on Multimodal InteractionResearch in automatic affect recognition has come a long way. This paper describes the fifth Emotion Recognition in the Wild (EmotiW) challenge 2017. EmotiW aims at providing a common benchmarking platform for researchers working on different aspects ...
Comments