Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol

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.

[1]  Gwen Littlewort,et al.  Automatic Recognition of Facial Actions in Spontaneous Expressions , 2006, J. Multim..

[2]  Gwen Littlewort,et al.  Multiple kernel learning for emotion recognition in the wild , 2013, ICMI '13.

[3]  Gayler, and David Hawking. Similarity-Aware Indexing for , 2009 .

[4]  Shiguang Shan,et al.  Partial least squares regression on grassmannian manifold for emotion recognition , 2013, ICMI '13.

[5]  Björn W. Schuller,et al.  The INTERSPEECH 2010 paralinguistic challenge , 2010, INTERSPEECH.

[6]  Razvan Pascanu,et al.  Combining modality specific deep neural networks for emotion recognition in video , 2013, ICMI '13.

[7]  Tamás D. Gedeon,et al.  Collecting Large, Richly Annotated Facial-Expression Databases from Movies , 2012, IEEE MultiMedia.

[8]  Björn W. Schuller,et al.  AVEC 2011-The First International Audio/Visual Emotion Challenge , 2011, ACII.

[9]  Roland Göcke,et al.  Finding Happiest Moments in a Social Context , 2012, ACCV.

[10]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Björn W. Schuller,et al.  OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[12]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[13]  Tamás D. Gedeon,et al.  Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[14]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Marian Stewart Bartlett,et al.  Weakly supervised pain localization using multiple instance learning , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[16]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[17]  Björn Schuller,et al.  Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.

[18]  Björn W. Schuller,et al.  Efficient Recognition of Authentic Dynamic Facial Expressions on the Feedtum Database , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[19]  Gwen Littlewort,et al.  A discriminative parts based model approach for fiducial points free and shape constrained head pose normalisation in the wild , 2014, IEEE Winter Conference on Applications of Computer Vision.

[20]  Gwen Littlewort,et al.  Toward Practical Smile Detection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.