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

Häftad, Engelska, 2017

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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.

Produktinformation

Utgivningsdatum2017-05-31
Mått155 x 235 x 10 mm
Vikt271 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor161
FörlagSpringer International Publishing AG
ISBN9783319592589

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Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it
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Människa – datorinteraktion inom Data och it

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.