by M Wimmer, BA. MacDonald, D Jayamuni and A Yadav
Abstract:
To be effective in the human world robots must respond to human emotional states. This paper focuses on the recognition of the six universal human facial expressions. In the last decade there has been successful research on facial expression recognition (FER) in controlled conditions suitable for human-computer interaction. However the human-robot scenario presents additional challenges including a lack of control over lighting conditions and over the relative poses and separation of the robot and human, the inherent mobility of robots, and stricter real time computational requirements dictated by the need for robots to respond in a timely fashion. Our approach imposes lower computational requirements by specifically adapting model-based techniques to the FER scenario. It contains adaptive skin color extraction, localization of the entire face and facial components, and specifically learned objective functions for fitting a deformable face model. Experimental evaluation reports a recognition rate of 70% on the Cohn-Kanade facial expression database, and 67% in a robot scenario, which compare well to other FER systems.
Reference:
Facial Expression Recognition for Human-robot Interaction – A Prototype (M Wimmer, BA. MacDonald, D Jayamuni and A Yadav), In 2\textbackslashtextsuperscriptnd Workshop Robot Vision. Lecture Notes in Computer Science. (R Klette, G Sommer, eds.), Springer, volume 4931/2008, 2008.
Bibtex Entry:
@inproceedings{wimmer_facial_2008,
author = {M Wimmer and BA. MacDonald and D Jayamuni and A Yadav},
title = {Facial Expression Recognition for Human-robot Interaction – A Prototype},
booktitle = {2{\textbackslash}textsuperscriptnd Workshop Robot Vision. Lecture
Notes in Computer Science.},
year = {2008},
editor = {Klette, Reinhard and Sommer, Gerald},
volume = {4931/2008},
pages = {139--152},
address = {Auckland, New Zealand},
month = {feb},
publisher = {Springer},
abstract = {To be effective in the human world robots must respond to human emotional
states. This paper focuses on the recognition of the six universal
human facial expressions. In the last decade there has been successful
research on facial expression recognition ({FER)} in controlled conditions
suitable for human-computer interaction. However the human-robot
scenario presents additional challenges including a lack of control
over lighting conditions and over the relative poses and separation
of the robot and human, the inherent mobility of robots, and stricter
real time computational requirements dictated by the need for robots
to respond in a timely fashion. Our approach imposes lower computational
requirements by specifically adapting model-based techniques to the
{FER} scenario. It contains adaptive skin color extraction, localization
of the entire face and facial components, and specifically learned
objective functions for fitting a deformable face model. Experimental
evaluation reports a recognition rate of 70\% on the Cohn-Kanade
facial expression database, and 67\% in a robot scenario, which compare
well to other {FER} systems.},
keywords = {facial expressions},
}