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.
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Gonzalez, Avelino J.
Bachelor of Science in Computer Engineering (B.S.P.E.)
College of Engineering and Computer Science
Dissertations, Academic -- Engineering and Computer Science;Engineering and Computer Science -- Dissertations, Academic
Length of Campus-only Access
Honors in the Major Thesis
Husk, Evan, "Imitating individualized facial expressions in a human-like avatar through a hybrid particle swarm optimization - tabu search algorithm" (2012). HIM 1990-2015. 1358.