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Neural Computing and Applications

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01.05.2014 | Original Article

Human face recognition based on ensemble of polyharmonic extreme learning machine

verfasst von: Jianwei Zhao, Zhenghua Zhou, Feilong Cao

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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Abstract

This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate.

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Titel: Human face recognition based on ensemble of polyharmonic extreme learning machine
verfasst von: Jianwei Zhao
Zhenghua Zhou
Feilong Cao
Publikationsdatum: 01.05.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1356-4

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