Gesture-Based affective computing on motion capture data

Authors:
Asha Kapur;Ajay Kapur;Naznin Virji-Babul;George Tzanetakis;Peter F. Driessen
Affiliations:
School of Medicine, Wake Forest University, North Carolina, United States;University of Victoria, Victoria, British Columbia, Canada;University of Victoria, Victoria, British Columbia, Canada;University of Victoria, Victoria, British Columbia, Canada;University of Victoria, Victoria, British Columbia, Canada
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 6
Cited 13

Coding, Analysis, Interpretation, and Recognition of Facial Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion

International Journal of Computer Vision
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Face to interface: facial affect in (hu)man and machine

Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Multimodal Human Emotion/Expression Recognition

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Toward computers that recognize and respond to user emotion

IBM Systems Journal

Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder

International Journal of Human-Computer Studies
Detecting Affect from Non-stylised Body Motions

ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Recognising Human Emotions from Body Movement and Gesture Dynamics

ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Manual annotation and automatic image processing of multimodal emotional behaviors: validating the annotation of TV interviews

Personal and Ubiquitous Computing
Recognizing affect from non-stylized body motion using shape of Gaussian descriptors

Proceedings of the 2010 ACM Symposium on Applied Computing
Automated analysis of non-verbal affective and social behaviour

Proceedings of the 3rd international workshop on Affective interaction in natural environments
Form as a cue in the automatic recognition of non-acted affective body expressions

ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Towards analysis of expressive gesture in groups of users: computational models of expressive social interaction

GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
What Does Touch Tell Us about Emotions in Touchscreen-Based Gameplay?

ACM Transactions on Computer-Human Interaction (TOCHI)
Natural interaction expressivity modeling and analysis

Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
The impact of vibrotactile biofeedback on the excessive walking sway and the postural control in elderly

Proceedings of the 4th Conference on Wireless Health
Discriminative functional analysis of human movements

Pattern Recognition Letters
Recognizing and regulating e-learners' emotions based on interactive Chinese texts in e-learning systems

Knowledge-Based Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.