Open Access

Human Hand Recognition Using IPCA-ICA Algorithm

EURASIP Journal on Advances in Signal Processing20072007:091467

https://doi.org/10.1155/2007/91467

Received: 3 July 2006

Accepted: 2 February 2007

Published: 22 March 2007

Abstract

A human hand recognition system is introduced. First, a simple preprocessing technique which extracts the palm, the four fingers, and the thumb is introduced. Second, the eigenpalm, the eigenfingers, and the eigenthumb features are obtained using a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA. This algorithm is based on merging sequentially the runs of two algorithms: the principal component analysis (PCA) and the independent component analysis (ICA) algorithms. It computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Third, a classification step in which each feature representation obtained in the previous phase is fed into a simple nearest neighbor classifier. The system was tested on a database of 20 people (100 hand images) and it is compared to other algorithms.

Keywords

Principal Component AnalysisCovarianceInformation TechnologyCovariance MatrixQuantum Information

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Authors’ Affiliations

(1)
Department of Computer Engineering, University of Balamand, Elkoura, Lebanon

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Copyright

© Issam Dagher et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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