Facial Expression Cloning with Fuzzy Membership Functions

This thesis describes the development and experimental results of a system to explore cloning of facial expressions between dissimilar face models, so new faces can be animated using the animations from existing faces. The system described in this thesis uses fuzzy membership functions and subtractive clustering to represent faces and expressions in an intermediate space. This intermediate space allows expressions for face models with different resolutions to be compared. The algorithm is trained for each pair of faces using particle swarm optimization, which selects appropriate weights and radii to construct the intermediate space. These techniques allow the described system to be more flexible than previous systems, since it does not require prior knowledge of the underlying implementation of the face models to function.

Grantor dc:publisher

Université d'Ottawa / University of Ottawa

Year dc:date

2013

Author dc:creator

• Santos, Patrick John

Contributors dc:contributor

• Petriu, Emil

• facial expresisons
• fuzzy logic
• clustering
• particle swarm optimization

Language dc:language

en

Identifier

http://dx.doi.org/10.20381/ruor-3311

OAI identifier oai:identifier

oai:ruor.uottawa.ca:10393/26260