Facial Expression Recognition for Human-robot Interaction – A Prototype (bibtex)
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},
}
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Facial Expression Recognition for Human-robot Interaction – A Prototype (bibtex)
Facial Expression Recognition for Human-robot Interaction – A Prototype (bibtex)
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},
}
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projects:facial_expressions

Analysis of Facial Expressions

As robots emerge from their classical domain - factories - to be included in every day life, they need to gain new abilities besides those needed in manufacturing. They need not only to support humans, but also be able to socialize with their users to enhance the interaction experience and allow for social bonding. Recent progress in the field of Computer Vision allows intuitive interaction via gesture or facial expressions between humans and technical systems. Recent research aims at enabling machines to utilize communication channels natural to human beings, such as gesture or facial expressions. Humans interpret emotion from video and audio information and heavily rely on this information during every-day communication. Therefore, knowledge about human behavior, intention, and emotion is necessary to construct convenient human-machine interaction mechanisms. The human face provides much of the information that is passed between humans in every-day communication. Although most of this information is passed on a subconscious level, we still rely on the interaction partner's facial expression to determine emotional state or attention to form a prediction of his or her reaction.

Project details

This project aims at determining facial expressions from camera images in real-time. Model-based image interpretation techniques have proven to be a successful method for extracting such high-level information from single images and image sequences. We rely on a model-based technique to determine the exact location of facial components such as eyes or eye brows in the image. Geometric models form an abstraction of real-world objects and contain knowledge about their properties, such as position, shape or texture. This representation of the image content facilitates and accelerates the subsequent interpretation task. In order to extract high-level information, model parameters have to be estimated that best describe the face within a given image. However, correctly estimated model parameters forms the basis of various more applications such as gaze detection or gender estimation.

Our demonstrator for facial expression recognition has been presented at several events with political audience and on TV. The face is detected and a 3D face model is fitted in real-time to extract the facial expression currently visible. We integrate the publicly available Candide-III face model and also rely on publicly available databases to train and evaluate classifiers for facial expression recognition. This contributes to the comparability of our approach with other research groups. Ekman and Friesen find six universal facial expressions that are expressed and interpreted independent from the cultural background, age or country of origin all over the world. The Facial Action Coding System (FACS) precisely describes the muscle activity within a human face that appear during the display of facial expressions. The Candide-III face model integrates the FACS-system in its model parameters.

Evidence suggests that feeling empathy for others is connected to the mirror neuron system and that emotional empathy, which is triggered by deriving the emotional state from facial expressions involves neural activity in the thalamus and cortical areas responsible of the face. Perception and display of facial expression form a closed loop in human-human communication, where the perception of the interaction partner's facial expression has influence on the display of the own facial expression. To research this also on the human-machine interface, we integrate our demonstrator in the Multi-Joint Action Scenario in the CoTeSys Central Robotics Lab. It is combined with the robot head EDDIE, provided by the Institute of Automatic Control Engineering, to form a closed-loop human-machine interaction scenario based on facial expression analysis and synthesis. In its current, preliminary state, the facial expression is merely mirrored, but future plans involve integrating a more complex emotional model on the robotic side.