Did I Get It Right: Head Gestures Analysis for Human-Machine Interactions

Authors:
Jürgen Gast;Alexander Bannat;Tobias Rehrl;Gerhard Rigoll;Frank Wallhoff;Christoph Mayer;Bernd Radig
Affiliations:
Department of Electrical Engineering and Information Technology, Institute for Human-Machine Communication,;Department of Electrical Engineering and Information Technology, Institute for Human-Machine Communication,;Department of Electrical Engineering and Information Technology, Institute for Human-Machine Communication,;Department of Electrical Engineering and Information Technology, Institute for Human-Machine Communication,;Department of Electrical Engineering and Information Technology, Institute for Human-Machine Communication,;Computer Science Department, Chair for Image Understanding and Knowledge-Based Systems, Technische Universität München, Munich, Germany 80290;Computer Science Department, Chair for Image Understanding and Knowledge-Based Systems, Technische Universität München, Munich, Germany 80290
Venue:
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Year:
2009

Citing 4
Cited 1

Automatic Analysis of Facial Expressions: The State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Detection in Video under Changing Illumination Conditions

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Robust Real-Time Face Detection

International Journal of Computer Vision
Learning Local Objective Functions for Robust Face Model Fitting

IEEE Transactions on Pattern Analysis and Machine Intelligence

A multimodal human-robot-dialog applying emotional feedbacks

ICSR'10 Proceedings of the Second international conference on Social robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a system for another input modality in a multimodal human-machine interaction scenario. In addition to other common input modalities, e.g. speech, we extract head gestures by image interpretation techniques based on machine learning algorithms to have a nonverbal and familiar way of interacting with the system. Our experimental evaluation proofs the capability of the presented approach to work in real-time and reliable.