Learning Individualized Facial Expressions in an Avatar with PSO and Tabu Search

摘要

This paper describes a method for automatically imitating a particular facial expression in an avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen's Facial Action Coding System (FACS). Using a neutral expression as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Particle Swarm Optimization algorithm and Tabu Search. Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. Results are described and discussed.