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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer-Verlag

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DOI

10.1001/archsurg.2012.2318

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1433-3058

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Multidirectional twodimensional PCA with matchin

Authors: Qi Zhu Yong Xu
Publish Date: 2012/01/31
Volume: 23, Issue: 1, Pages: 169-174
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Abstract

The traditional matrixbased feature extraction methods that have been widely used in face recognition essentially work on the facial image matrixes only in one or two directions For example 2DPCA can be seen as the rowbased PCA and only reflects the information in each row and some structure information cannot be uncovered by it In this paper we propose the directional 2DPCA that can extract features from the matrixes in any direction To effectively use all the features extracted by the D2DPCA we combine a bank of D2DPCA performed in different directions to develop a matching score level fusion method named multidirectional 2DPCA for face recognition The results of experiments on AR and FERET datasets show that the proposed method can obtain a higher accuracy than the previous matrixbased feature extraction methods

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