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Optical Engineering

Novel dynamic Bayesian networks for facial action element recognition and understanding
Author(s): Wei Zhao; Sang-Woong Lee; Jeong-Seon Park; Dong-You Choi
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Paper Abstract

In daily life, language is an important tool of communication between people. Besides language, facial action can also provide a great amount of information. Therefore, facial action recognition has become a popular research topic in the field of human-computer interaction (HCI). However, facial action recognition is quite a challenging task due to its complexity. In a literal sense, there are thousands of facial muscular movements, many of which have very subtle differences. Moreover, muscular movements always occur simultaneously when the pose is changed. To address this problem, we first build a fully automatic facial points detection system based on a local Gabor filter bank and principal component analysis. Then, novel dynamic Bayesian networks are proposed to perform facial action recognition using the junction tree algorithm over a limited number of feature points. In order to evaluate the proposed method, we have used the Korean face database for model training. For testing, we used the CUbiC FacePix, facial expressions and emotion database, Japanese female facial expression database, and our own database. Our experimental results clearly demonstrate the feasibility of the proposed approach.

Paper Details

Date Published: 1 December 2011
PDF: 14 pages
Opt. Eng. 50(12) 127209 doi: 10.1117/1.3662426
Published in: Optical Engineering Volume 50, Issue 12
Show Author Affiliations
Wei Zhao, Chosun Univ. (Korea, Republic of)
Sang-Woong Lee, Chosun Univ. (Korea, Republic of)
Jeong-Seon Park, Chonnam National Univ. (Korea, Republic of)
Dong-You Choi, Chosun Univ. (Korea, Republic of)


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