Facial Expression Recognition
Computers in these days try to interpret certain human characteristics so as to react better. These characteristics include facial expressions, eyes gaze, body gait, speech etc. . Many applications such as virtual reality, videoconferencing, user profiling, customer satisfaction studies for broadcast and web services and interfaces for people with special needs require efficient facial expression recognition in order to achieve the desired results.
The basic facial expressions are defined as six anger, disgust, fear, happiness, sadness and surprise. A set of muscle movements (Facial Action Units-FAUs) was created to produce those facial expressions, forming the Facial Action Coding System ( FACS ).
Facial expressions are generally hard to recognize as:
A novel method that performs facial expression recognition has been developed:
The accuracy achieved is equal to 99.7% when using multi-class SVMs for facial expression recognition and to 95.1% when using two-class SVMs for FAUs detection and afterwards facial expression recognition.
I. Kotsia and I. Pitas, "Real time facial expression recognition from video sequences using Support Vector Machines", in Proc. of Visual Communications and Image Processing (VCIP 2005), Beijing, China, 12-15 July, 2005.
I. Kotsia and I. Pitas, "Real time facial expression recognition from image sequences using Support Vector Machines", in Proc. of IEEE Int. Conf. on Image Processing (ICIP 2005), Genova, Italy, 11-14 September, 2005.
I. Kotsia and I. Pitas, "Facial Expression Recognition in Image Sequences using Geometric Deformation Features and Support Vector Machines", IEEE Transactions on Image Processing, January, 2007.
SIMILAR - The European research taskforce creating human-machine interfaces SIMILAR to human-human communication, IST, FP6
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