You are here

IMITATING INDIVIDUALIZED FACIAL EXPRESSIONS IN A HUMAN-LIKE AVATAR THROUGH A HYBRID PARTICLE SWARM OPTIMIZATION - TABU SEARCH ALGORITHM

Download pdf | Full Screen View

Date Issued:
2012
Abstract/Description:
This thesis describes a machine learning method for automatically imitating a particular person's facial expressions in a human-like 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 face 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 Kennedy and Eberhart's Particle Swarm Optimization algorithm (PSO) and Glover's Tabu Search (TS). 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. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work.
Title: IMITATING INDIVIDUALIZED FACIAL EXPRESSIONS IN A HUMAN-LIKE AVATAR THROUGH A HYBRID PARTICLE SWARM OPTIMIZATION - TABU SEARCH ALGORITHM.
23 views
18 downloads
Name(s): Husk, Evan, Author
Gonzalez, Avelino, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2012
Publisher: University of Central Florida
Language(s): English
Abstract/Description: This thesis describes a machine learning method for automatically imitating a particular person's facial expressions in a human-like 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 face 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 Kennedy and Eberhart's Particle Swarm Optimization algorithm (PSO) and Glover's Tabu Search (TS). 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. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work.
Identifier: CFH0004286 (IID), ucf:44949 (fedora)
Note(s): 2012-12-01
B.S.P.E.
Engineering and Computer Science, Dept. of Electrical Engineering and Computer Science
Bachelors
This record was generated from author submitted information.
Subject(s): avatar
hybrid
algorithm
FACS
PSO
swarm
TS
facial
expressions
optimization
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFH0004286
Restrictions on Access: public
Host Institution: UCF

In Collections