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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2014

01.10.2014 | Original Article

Enhancing sparsity via full rank decomposition for robust face recognition

verfasst von: Yuwu Lu, Jinrong Cui, Xiaozhao Fang

Erschienen in: Neural Computing and Applications | Ausgabe 5/2014

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Abstract

In this paper, we propose a fast and robust face recognition method named enhancing sparsity via full rank decomposition. The proposed method first represents the test sample as a linear combination of the training data as the same as sparse representation, then make a full rank decomposition of the training data matrix. We obtain the generalized inverse of the training data matrix and then solve the general solution of the linear equation directly. For obtaining the optimum solution to represent the test sample, we use the least square method to solve it. We classify the test sample into the class which has the minimal reconstruction error. Our method can solve the optimum solution of the linear equation, and it is more suitable for face recognition than sparse representation classifier. The extensive experimental results on publicly available face databases demonstrate the effectiveness of the proposed method for face recognition.
Vorheriger Artikel Artificial neural networks to identify naturally existing disease severity status
Nächster Artikel Global and decomposition evolutionary support vector machine approaches for time series forecasting

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Metadaten
Titel
Enhancing sparsity via full rank decomposition for robust face recognition
verfasst von
Yuwu Lu
Jinrong Cui
Xiaozhao Fang
Publikationsdatum
01.10.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1582-4

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