A new approach for personal identification using hand geometry based upon geometrical and shape features is presented. We propose a new pegless hand geometry verification system where the users are free to put their hand in arbitrary fashion. A Linear Discirminant Analysis if applied to the raw data in order to perform a best clustering of the feature space. The combination of three different neural network classifiers (unsupervised SOM, supervised SOM and LVQ) gives 0.35% FAR and 0.15% FRR. The method has been tested on a large size database of 1400 images for training and 1400 for test from 280 individuals suitable for medium and low security applications.
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