Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction: 5th IAPR TC 9 Workshop, MPRSS 2018, Beijing, China, August 20, 2018, Revised Selected Papers
Author:   Friedhelm Schwenker ,  Stefan Scherer
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Volume:   11377
ISBN:  

9783030209834


Pages:   117
Publication Date:   15 May 2019
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction: 5th IAPR TC 9 Workshop, MPRSS 2018, Beijing, China, August 20, 2018, Revised Selected Papers


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Overview

This book constitutes the refereed post-workshop proceedings of the 5th IAPR TC9 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2018, held in Beijing, China, in August 2018.  The 10 revised papers presented in this book focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition and pain intensity estimation, especially the question how to distinguish between human emotions from pain or stress induced by pain is discussed.

Full Product Details

Author:   Friedhelm Schwenker ,  Stefan Scherer
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Volume:   11377
Weight:   0.454kg
ISBN:  

9783030209834


ISBN 10:   3030209830
Pages:   117
Publication Date:   15 May 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Multi-Focus Image Fusion with PCA Filters of PCANet.- An Image Captioning Method for Infant Sleeping Environment Diagnosis.- A First-Person Vision Dataset of Office Activities.- Perceptual Judgments to Detect Computer Generated Forged Faces in Social Media.- Combining Deep and Hand-crafted Features for Audio-based Pain Intensity Classification.- Deep Learning Algorithms for Emotion Recognition on Low Power Single Board Computers.- Improving Audio-Visual Speech Recognition Using Gabor Recurrent Neural Networks.- Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity.- Visualizing Facial Expression Features of Pain and Emotion Data.

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