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Adaptive Body Gesture Representation for Automatic Emotion Recognition

Adaptive Body Gesture Representation for Automatic Emotion Recognition Adaptive Body Gesture Representation for Automatic Emotion Recognition ` STEFANO PIANA, ALESSANDRA STAGLIANO, FRANCESCA ODONE, ` and ANTONIO CAMURRI, DIBRIS--Universita degli Studi di Genova We present a computational model and a system for the automated recognition of emotions starting from full-body movement. Three-dimensional motion data of full-body movements are obtained either from professional optical motion-capture systems (Qualisys) or from low-cost RGB-D sensors (Kinect and Kinect2). A number of features are then automatically extracted at different levels, from kinematics of a single joint to more global expressive features inspired by psychology and humanistic theories (e.g., contraction index, fluidity, and impulsiveness). An abstraction layer based on dictionary learning further processes these movement features to increase the model generality and to deal with intraclass variability, noise, and incomplete information characterizing emotion expression in human movement. The resulting feature vector is the input for a classifier performing real-time automatic emotion recognition based on linear support vector machines. The recognition performance of the proposed model is presented and discussed, including the tradeoff between precision of the tracking measures (we compare the Kinect RGB-D sensor and the Qualisys motion-capture system) versus dimension of the training dataset. The resulting model and system have been successfully http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Adaptive Body Gesture Representation for Automatic Emotion Recognition

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2818740
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Abstract

Adaptive Body Gesture Representation for Automatic Emotion Recognition ` STEFANO PIANA, ALESSANDRA STAGLIANO, FRANCESCA ODONE, ` and ANTONIO CAMURRI, DIBRIS--Universita degli Studi di Genova We present a computational model and a system for the automated recognition of emotions starting from full-body movement. Three-dimensional motion data of full-body movements are obtained either from professional optical motion-capture systems (Qualisys) or from low-cost RGB-D sensors (Kinect and Kinect2). A number of features are then automatically extracted at different levels, from kinematics of a single joint to more global expressive features inspired by psychology and humanistic theories (e.g., contraction index, fluidity, and impulsiveness). An abstraction layer based on dictionary learning further processes these movement features to increase the model generality and to deal with intraclass variability, noise, and incomplete information characterizing emotion expression in human movement. The resulting feature vector is the input for a classifier performing real-time automatic emotion recognition based on linear support vector machines. The recognition performance of the proposed model is presented and discussed, including the tradeoff between precision of the tracking measures (we compare the Kinect RGB-D sensor and the Qualisys motion-capture system) versus dimension of the training dataset. The resulting model and system have been successfully

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Mar 9, 2016

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