TY - GEN
T1 - Recognizing facial expressions using model-based image interpretation
AU - Wimmer, Matthias
AU - Mayer, Christoph
AU - Radig, Bernd
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=67650499057&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-00525-1_33
DO - 10.1007/978-3-642-00525-1_33
M3 - Conference contribution
AN - SCOPUS:67650499057
SN - 3642005241
SN - 9783642005244
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 328
EP - 339
BT - Multimodal Signals
T2 - COST Action 2102 and euCognition International School on Multimodal Signals: Cognitive and Algorithmic Issues
Y2 - 21 April 2008 through 26 April 2008
ER -