A robust scheme for facial analysis and expression recognition
M. Wallace, S. Ioannou, K. Karpouzis and S. Kollias
Image, Video and Multimedia Systems Lab, School of Electrical
and Computer Engineering, National
Technical University of Athens, Athens, Greece
Facial analysis includes a number of processing steps which attempt to
detect or track the
face, to locate characteristic facial regions such as eyes, mouth and
nose on it, to extract and
follow the movement of facial features, such as characteristic points
in these regions, or
model facial gestures using anatomic information about the face.
Due to noise, illumination variations and low resolution capturing devices,
the detection of facial features, and consequently, facial feature points,
can be inaccurate. For this reason, mechanisms are required that can automatically
evaluate the quality of each computed mask, assigning a confidence level
to it. The emotion recognition system can take advantage of each features
confidence level when analyzing them. Exploitation of anthropometric knowledge,
in the form of a set of criteria, evaluating the relation of the extracted
features can form such a mechanism.
The detected facial features are used to extract the Feature Points considered
in the definition of the Facial Animation Parameters (FAPs), to be used
as input features to the recognition system. Each FP inherits the confidence
level of the feature from which it derives. FAPs are estimated via the
comparison of the FPs of the examined frame to the FPs of a frame that
is known to be neutral, i.e. a frame which displays no facial deformations.
A variety of techniques can be used to recognize the underlying emotional
states, based on analysis of the FAP features extracted from the users
face. These include neural network classifiers, clustering techniques,
neurofuzzy networks and possibilistic approaches. Techniques that can
transfer variations of the FAP variables into rules and adapt these rules
to specific users characteristics, such as clustering and neurofuzzy
methods, or handle uncertainty, such as possibilistic methods are mainly
analyzed and used in the paper.
A variety of simulation studies is presented using acted and real life
data, which illustrate
the good performance of the presented system and of the included techniques.
Paper presented
at Measuring Behavior 2005
, 5th International Conference on Methods and Techniques
in Behavioral Research, 30 August - 2 September 2005, Wageningen, The
Netherlands.
© 2005 Noldus
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