NDLI: Human face recognition based on ensemble of polyharmonic extreme learning machine

Content Provider	SpringerLink
Author	Zhao, Jianwei Zhou, Zhenghua Cao, Feilong
Copyright Year	2013
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.
Starting Page	1317
Ending Page	1326
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	24
Issue Number	6
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2013-02-15
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Human face recognition Ensemble of polyharmonic extreme learning machine Fast discrete curvelet transform 2-Dimension principal component analysis Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Software Artificial Intelligence

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