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Collaborative Local Active Appearance Models for Illuminated Face Images  

Yang, Jun-Young (연세대학교 컴퓨터과학과)
Ko, Jae-Pil (금오공과대학교 컴퓨터공학부)
Byun, Hye-Ran (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.10, 2009 , pp. 816-824 More about this Journal
Abstract
In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.
Keywords
Local Active Appearance Models; Face Illumination Compensation; Face Alignment; Face Recognition;
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