Human Hand Recognition Using IPCA-ICA Algorithm
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Research Article Human Hand Recognition Using IPCA-ICA Algorithm Issam Dagher, William Kobersy, and Wassim Abi Nader Department of Computer Engineering, University of Balamand, Elkoura, Lebanon Received 3 July 2006; Revised 21 November 2006; Accepted 2 February 2007 Recommended by Satya Dharanipragada 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. Copyright © 2007 Issam Dagher et al. 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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INTRODUCTION
Biometrics is an emerging technology [1, 2] that is used to identify people by their physical and/or behavioral characteristics and, so, inherently requires that the person to be identified is physically present at the point of identification. The physical characteristics of an individual that can be used in biometric identification/verification systems are fingerprint [3, 4], hand geometry [5, 6], palm print [7–9], face [4, 10], iris [11, 12], retina [13], and the ear [14]. The behavioral characteristics are signature [12], lip movement [15], speech [16], keystroke dynamics [1, 2], gesture [1, 2], and the gait [1, 2]. A single physical or behavioral characteristic of an individual can sometimes be insufficient for identification. For this reason, multimodal biometric systems—that is, systems that integrate two or more different biometrics characteristics—are being developed to provide an acceptable performance, and increase the reliability of decisions. The human hand contains a wide variety of features, for example, shape, texture, and principal palm lines—that can be used by biometric systems. Features extracted by projecting palm images into the subspace obtained by the PCA transform are called eigenpalm features, whereas those extracted by projecting images of fingers and thumb are called eigenfinger and eigenthumb features. This paper merges sequen-
tially two techniques based on principal component analysis and independe
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