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Article Dans Une Revue ACM Transactions on Multimedia Computing, Communications and Applications Année : 2018

Introduction to the Special Section on Multimedia Computing and Applications of Socio-Affective Behaviors in the wild

Résumé

Affective Computing and Social Signal Processing are two developing fields of research that promise to revolutionise the way humans interact with multimedia and technology in general. Affective Computing is the science of creating emotionally aware systems, including the automatic analysis of affect and expressive behaviours. Whereas social signal processing addresses all verbal and non-verbal communicative signalling during social interactions, be they of an affective nature or not. Despite the scientific and technological revolutions of the last decades in these fields, there is still an urgent need to advance emotion and social behaviours recognition for multimodal interface interaction to a level where systems are able to deal with large volumes of non-prototypical naturalistic behaviour collected in ecologically valid environments, as this is exactly the type of data that applications would have to face in the real world. The first article of this special section addresses the analysis of malicious forms of communication in online social networks. Securing multimedia sharing and phishing attempts in social networks has become a critical issue with the rapidly increasing JR exploited two Twitter datasets to classify users as being human, legitimate robot or malicious robot. In the proposed method, occurrence of the terms present in the tweets are weighted by lexicon based coefficients and features are decomposed by a Discrete Wavelet Transform to feed a model based on a Random Forest classifier. An accuracy above 93% is reported for the detection of the three types of users (human, bot, malicious bot), with better results when the Tweets are related to a specific hashtag (Super Bowl event), in comparison with miscellaneous themes. The second article focuses on pose-invariant representation learning of facial expressions for emotion recognition. In the article "Spatially Coherent Feature Learning for Pose-invariant Facial Expression Recognition", Feifei Zhang, Qirong Mao, Xiangjun Shen, Yongzhao Zhan and Ming Dong, exploit a 3D pose normalisation technique that helps preserving identity information while avoiding the use of multiple models for pose-specific training and parameter tuning. In the proposed method, a sequence of key regions in a synthetic frontal face image is selected for unsupervised representation learning. Dependencies between regions of the face are specifically encoded by introducing a linkage structure over the learning-based features and the corresponding geometry information of each key region. Experimental evaluations were conducted on the BU-3DFE and SFEW datasets, and showed that the proposed model achieved superior results than others existing methods on those two datasets. We hope that this special section will bring inspirational ideas to researchers working in the domains of affective computing and/or social signal processing. We would like to thank all the authors who submitted their work to the special section, as well as the reviewers who contributed with their comments and observations to the high quality of the selected papers. We would also especially thank the editorial members of ACM TOMM, Prof. Alberto Del Bimbo and Prof. Stefano Berretti, for their support and kind help in the realisation of this special section.
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Dates et versions

hal-01993366 , version 1 (24-01-2019)

Identifiants

  • HAL Id : hal-01993366 , version 1

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Fabien Ringeval, Björn Schuller, Michel Valstar, Jonathan Gratch, Roddy Cowie, et al.. Introduction to the Special Section on Multimedia Computing and Applications of Socio-Affective Behaviors in the wild. ACM Transactions on Multimedia Computing, Communications and Applications, 2018, 14 (1s), pp.1-2. ⟨hal-01993366⟩
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