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 / edited by Friedhelm Schwenker, Stefan Scherer.

Format:

Book

Contributor:

Schwenker, Friedhelm, editor.

Scherer, Stefan, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 11377.

Lecture Notes in Artificial Intelligence ; 11377

Language:

English

Subjects (All):

Artificial intelligence.

Optical data processing.

Computer networks.

User interfaces (Computer systems).

Artificial Intelligence.

Image Processing and Computer Vision.

Computer Communication Networks.

User Interfaces and Human Computer Interaction.

Local Subjects:

Artificial Intelligence.

Image Processing and Computer Vision.

Computer Communication Networks.

User Interfaces and Human Computer Interaction.

Physical Description:

1 online resource (VII, 117 pages) : 117 illustrations, 32 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-030-20984-1

9783030209841

Access Restriction:

Restricted for use by site license.