University of Bahrain
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Human Face Recognition Using Class-wise Two-dimensional Principal Component Analysis

Show simple item record Alsaqre, Falah 2020-02-29T20:41:04Z 2020-02-29T20:41:04Z 2020-03-01
dc.identifier.issn 2210-142X
dc.description.abstract Two-dimensional principal component analysis (2DPCA) and its variants have been successfully used for the task of face recognition (FR). However, one of the major limitations of 2DPCA-based FR methods is that they only consider the holistic information of a given training dataset, ignoring both class-specific discriminant information and class-separation components, which could further improve recognition performance. To address this limitation, this paper suggests a class-wise 2DPCA (CW2DPCA) framework that seeks to model class-specific subspaces, where each subspace retains the discriminatory information of a particular class, as well as class separability information. In this way, CW2DPCA not only feeds discriminative representations of facial images to the classification model, but also enables a high degree of separation between the different classes present in the training dataset. Experimental evaluation on two face datasets proved the effectiveness of the CW2DPCA in FR. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Face recognition en_US
dc.subject Two-dimensional principal component analysis (2DPCA) en_US
dc.subject Feature extraction en_US
dc.title Human Face Recognition Using Class-wise Two-dimensional Principal Component Analysis en_US
dc.volume 9 en_US
dc.issue 2 en_US
dc.pagestart 335 en_US
dc.pageend 343 en_US
dc.contributor.authorcountry Iraq en_US
dc.contributor.authoraffiliation Al-Hikma University College en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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