by M Wimmer, C Mayer and B Radig
Abstract:
Even if electronic devices widely occupy our daily lives, human-machine interaction still lacks intuition. Therefore, researchers intend to resolve these shortcomings by augmenting traditional systems with aspects of human-human interaction and consider human emotion, behavior, and intention. This publication focusses on one aspect of this challenge: recognizing facial expressions. Our approach achieves real-time performance and provides robustness for real-world applicability. This computer vision task comprises of various phases for which it exploits model-based techniques that accurately localize facial features, seamlessly track them through image sequences, and finally infer facial expressions visible. We specifically adapt state-of-the-art techniques to each of these challenging phases. Our system has been successfully presented to industrial, political, and scientific audience in various events.
Reference:
Recognizing Facial Expressions Using Model-based Image Interpretation (M Wimmer, C Mayer and B Radig), In Verbal and Nonverbal Communication Behaviours, COST Action 2102 International Workshop, 2008. (Invited Paper)
Bibtex Entry:
@inproceedings{wimmer_recognizing_2008,
author = {M Wimmer and C Mayer and B Radig},
title = {Recognizing Facial Expressions Using Model-based Image Interpretation},
booktitle = {Verbal and Nonverbal Communication Behaviours, {COST} Action 2102
International Workshop},
year = {2008},
address = {Vietri sul Mare, Italy},
month = {apr},
abstract = {Even if electronic devices widely occupy our daily lives, human-machine
interaction still lacks intuition. Therefore, researchers intend
to resolve these shortcomings by augmenting traditional systems with
aspects of human-human interaction and consider human emotion, behavior,
and intention. This publication focusses on one aspect of this challenge:
recognizing facial expressions. Our approach achieves real-time performance
and provides robustness for real-world applicability. This computer
vision task comprises of various phases for which it exploits model-based
techniques that accurately localize facial features, seamlessly track
them through image sequences, and finally infer facial expressions
visible. We specifically adapt state-of-the-art techniques to each
of these challenging phases. Our system has been successfully presented
to industrial, political, and scientific audience in various events.},
keywords = {facial expressions},
}