Novel dynamic Bayesian networks for facial action element recognition and understanding

Wei Zhao; Sang-Woong Lee; Jeong-Seon Park; Dong-You Choi

doi:10.1117/1.3662426

1 December 2011 Novel dynamic Bayesian networks for facial action element recognition and understanding

Wei Zhao, Sang-Woong Lee, Jeong-Seon Park, Dong-You Choi

Author Affiliations +

Optical Engineering, Vol. 50, Issue 12, 127209 (December 2011). https://doi.org/10.1117/1.3662426

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.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Wei Zhao, Sang-Woong Lee, Jeong-Seon Park, and Dong-You Choi "Novel dynamic Bayesian networks for facial action element recognition and understanding," Optical Engineering 50(12), 127209 (1 December 2011). https://doi.org/10.1117/1.3662426

Published: 1 December 2011

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KEYWORDS

Databases

Facial recognition systems

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Expectation maximization algorithms

Data modeling

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Image filtering

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