FaceReader: an online facial expression recognition system
M. den Uyl, H. van Kuilenburg and E. Lebert
VicarVision, Amsterdam, The Netherlands
Facial expressions can contain a great deal of information and the desire
to automatically extract this information has been continuously increasing.
Several applications for automatic facial expression recognition can be
found in the field of human-computer interaction. In every day human-to-human
interaction, information is exchanged in a highly multi-modal way in which
speech only plays a modest role. An effective automatic expression recognition
system could take human-computer interaction to the next level.
Automatic expression analysis can be of particular relevance for a number
of expression monitoring applications where it would be undesirable or
even infeasible to manually annotate the available data. For instance,
the reaction of people in test-panels could be automatically monitored
and forensic investigation could benefit from a method to automatically
detect signs of extreme emotions, fear or aggression as an early warning
system.
The classification accuracy for an automatic system is however largely
limited by the image quality, lighting conditions and the orientation
of the depicted face. These problems can be partially overcome by using
a holistic model based approach called the Active Appearance Model. In
this approach, a face model is created in which lighting and pose are
compactly represented. This model can then be analyzed and classified
on a number of categories.
The FaceReader system to be demonstrated can classify facial expressions
from one of the 'basic' emotional categories joy, anger, sadness, surprise,
fear and disgust real-time and with remarkable accuracy. A performance
of up to 90% correct has been achieved using frontal face images, making
it one of the best performing systems in the field. The system can easily
be trained to classify other facial expression categories, as is illustrated
by its ability to individually evaluate several Facial Action Units. Furthermore,
the system can also be used to classify other properties, such as the
gender or age of the depicted person. The demonstration will consist of
a poster presentation together with an online demonstration of our FaceReader
system.
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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