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.
Highlights
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 basis vectors obtained by the incremental principal component analysis (IPCA)-independent component analysis (ICA) algorithm will have more efficiency or contain more information than those chosen by the batch algorithm
To demonstrate the effectiveness of the IPCA-ICA algorithm on the human hand recognition problem, a database consisting of 100 templates (20 users, 5 templates per user) was utilized
Summary
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. 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 traditional PCA [17] algorithm computes eigenvectors and eigenvalues for a sample covariance matrix derived from a well-known given image data matrix, by solving an eigenvalue system problem. The incremental principal component method updates the eigenvectors each time a new image is introduced. The idea of using a real time process becomes very efficient in order to compute the principal independent components for observations arriving sequentially. Each eigenvector or principal component will be updated, using FastICA algorithm, to a non-Gaussian component. The idea of using a real time process becomes very efficient in order to compute the principal independent components for observations (faces) arriving sequentially
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