Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
4th IAPR TC 9 Workshop, MPRSS 2016, Cancun, Mexico, December 4, 2016, Revised Selected Papers
Published on 31. May 2017
Book
Paperback/Softback
VIII, 161 pages
978-3-319-59258-9 (ISBN)
Description
This book constitutes the thoroughly refereed post-workshop proceedings of the Fourth IAPR TC9 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016, held in Cancun, Mexico, in December 2016.
The 13 revised papers presented focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition, user identification, and recognition of human activities.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
61 s/w Abbildungen
VIII, 161 p. 61 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 10 mm
Weight
271 gr
ISBN-13
978-3-319-59258-9 (9783319592589)
DOI
10.1007/978-3-319-59259-6
Schweitzer Classification
Other editions
Additional editions
Friedhelm Schwenker | Stefan Scherer
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
4th IAPR TC 9 Workshop, MPRSS 2016, Cancun, Mexico, December 4, 2016, Revised Selected Papers
E-Book
05/2017
Springer
€53.49
Available for download
Content
Active Shape Model Vs. Deep Learning for Facial Emotion Recognition in Security.- Bimodal Recognition of Cognitive Load Based on Speech and Physiological Changes.- Human Mobility-Pattern Discovery and Next-Place Prediction from GPS data.- Fusion Architectures for Multimodal Cognitive Load Recognition.- Face Recognition in Home Security System Using Tensor Decomposition Based on Radix Hierarchical SVD.- Performance analysis of gesture recognition classifiers for building a human robot interface.- On Automatic Question Answering Using Efficient Primal-dual Models.- Hierarchical Bayesian Multiple Kernel Learning Based Feature Fusion for Action Recognition.- Audio Visual Speech Recognition Using Deep Recurrent Neural Networks.- Audio-Visual Recognition of Pain Intensity.- The Sense Emotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Pain- and Emotion-Recognition System.- Photometric Stereo for 3D face reconstruction using non linear illumination models.- Recursively Measured Action Units.