Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction [electronic resource] : First IAPR TC3 Workshop, MPRSS 2012, Tsukuba, Japan, November 11, 2012, Revised Selected Papers / edited by Friedhelm Schwenker, Stefan Scherer, Louis-Philippe Morency.

Modelling Social Signals -- Generative Modelling of Dyadic Conversations: Characterization of Pragmatic Skills During Development Age -- Social Coordination Assessment: Distinguishing between Shape and Timing -- Social Signals in Facial Expressions -- A Novel LDA and HMM-Based Technique for Emotion Recognition from Facial Expressions -- Generation of Facial Expression for Communication Using Elfoid with Projector -- Eye Localization from Infrared Thermal Images -- Analysis of Speech and Physiological Speech -- The Effect of Fuzzy Training Targets on Voice Quality Classification -- Physiological Effects of Delayed System Response Time on Skin Conductance -- A Non-invasive Multi-sensor Capturing System for Human Physiological and Behavioral Responses Analysis -- Motion Analysis and Activity Recognition -- 3D Motion Estimation of Human Body from Video with Dynamic Camera Work -- Motion History of Skeletal Volumes and Temporal Change in Bounding Volume Fusion for Human Action Recognition -- Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance Videos -- Multimodal Fusion -- Using the Transferable Belief Model for Multimodal Input Fusion in Companion Systems -- Fusion of Fragmentary Classifier Decisions for Affective State Recognition.

This book constitutes the thoroughly refereed post-workshop proceedings of the First IAPR TC3 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS2012), held in Tsukuba, Japan in November 2012, in collaboration with the NLGD Festival of Games. The 21 revised papers presented during the workshop cover topics on facial expression recognition, audiovisual emotion recognition, multimodal Information fusion architectures, learning from unlabeled and partially labeled data, learning of time series, companion technologies and robotics.