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Published Articles >> Table of Contents >> Abstract
IEEE International Workshop on Analysis and Modeling of Faces and Gestures
p. 120
Component-based LDA Method for Face Recognition with One Training Sample
Jian Huang, Hong Kong Baptist University
Pong C Yuen, Hong Kong Baptist University
Wen-Sheng Chen, Hong Kong Baptist University; Shenzhen University
J H Lai, ZhongShan University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AMFG.2003.1240833
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| Abstract |
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Many face recognition algorithms/systems have been
developed in the last decade and excellent performances
are also reported when there is sufficient number of
representative training samples. In many real-life
applications, only one training sample is available.
Under this situation, the performance of existing
algorithms will be degraded dramatically or the
formulation is incorrect, which in turn, the algorithm
cannot be implemented. In this paper, we propose a
component-based linear discriminant analysis (LDA)
method to solve the one training sample problem. The
basic idea of the proposed method is to construct local
facial feature component bunches by moving each local
feature region in four directions. In this way, we not only
generate more samples, but also consider the face
detection localization error while training. After that, we
employ a sub-space LDA method, which is tailor-made
for small number of training samples, for the local
feature projection to maximize the discrimination power.
Finally, combining the contributions of each local feature
draws the recognition decision. FERET database is used
for evaluating the proposed method and results are
encouraging.
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 Additional Information
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Citation:
Jian Huang, Pong C Yuen, Wen-Sheng Chen, J H Lai,
"Component-based LDA Method for Face Recognition with One Training Sample,"
amfg,
p. 120,
IEEE International Workshop on Analysis and Modeling of Faces and Gestures,
2003
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