Deformable templates for features extraction from medical images
ECCV 90 Proceedings of the first european conference on Computer vision
Model-based image interpretation using genetic algorithms
Image and Vision Computing - Special issue: BMVC 1991
Feature extraction from faces using deformable templates
International Journal of Computer Vision
Image warping by radial basis functions: applications to facial expressions
CVGIP: Graphical Models and Image Processing
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry-driven photorealistic facial expression synthesis
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Statistical synthesis of facial expressions for the portrayal of emotion
Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Parameterized facial expression synthesis based on MPEG-4
EURASIP Journal on Applied Signal Processing
Fast learning in networks of locally-tuned processing units
Neural Computation
Eye synthesis using the eye curve model
Image and Vision Computing
Expression transfer for facial sketch animation
Signal Processing
Synthesis of emotional expressions specific to facial structure
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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This paper details a procedure for generating a function which maps an image of a neutral face to one depicting a desired expression independent of age, sex, or skin colour. Facial expression synthesis is a growing and relatively new domain within computer vision. One of the fundamental problems when trying to produce accurate expression synthesis in previous approaches is the lack of a consistent method for measuring expression. This inhibits the generation of a universal mapping function. This paper advances this domain by the introduction of the Facial Expression Shape Model (FESM) and the Facial Expression Texture Model (FETM). These are statistical models of facial expression based on anatomical analysis of expression called the Facial Action Coding System (FACS). The FESM and the FETM allow for the generation of a universal mapping function. These models provide a robust means for upholding the rules of the FACS and are flexible enough to describe subjects that are not present during the training phase. We use these models in conjunction with several Artificial Neural Networks (ANN) to generate photo-realistic images of facial expressions.