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Type: Conference paper
Title: Face Recognition from Video using Active Appearance Model Segmentation
Author: Faggian, N.
Paplinski, A.
Chin, T.J.
Citation: 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006: pp.287-290
Publisher: IEEE
Publisher Place: Online
Issue Date: 2006
ISBN: 0769525210
Conference Name: International Conference on Pattern Recognition (18th : 2006 : Hong Kong)
Statement of
Nathan Faggian, Andrew Paplinski and Tat-Jun Chin
Abstract: Face recognition from video can be improved if good face segmentation of the subject under test is achieved. Many video based face recognition rely on simple background modeling and coarse alignment strategies for segmentation. This work presents a face recognition from video framework based on using active appearance models (AAM) to achieve accurate face segmentation and consistent shape free representation across a video sequence. The segmentation provided by the AAM can be effectively normalized (morphed) to a mean shape. The resulting sub-image can then be delivered to conventional face recognition from video algorithms for robust classification. We present preliminary results on a dataset of 17 individuals and outline the problems encountered in this approach
RMID: 0020093452
DOI: 10.1109/ICPR.2006.526
Appears in Collections:Computer Science publications

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