Title

Learning Individualized Facial Expressions In An Avatar With Pso And Tabu Search

Abstract

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. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

12-13-2013

Publication Title

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

Number of Pages

76-81

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84889798845 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889798845

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