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http://dx.doi.org/10.6109/jkiice.2010.14.9.1972

Robust Face and Facial Feature Tracking in Image Sequences  

Jang, Kyung-Shik (동의대학교 멀티미디어공학과)
Lee, Chan-Hee (동의대학교 디지털미디어공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.14, no.9, 2010 , pp. 1972-1978 More about this Journal
Abstract
AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.
Keywords
AAM; SIFT; eye detection; Genetic algorithm;
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Times Cited By KSCI : 1  (Citation Analysis)
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