Peg-Free Hand Geometry Recognition Using Hierarchical Geomrtry and Shape Matching

We propose a feature-based hierarchical framework for hand geometry recognition, based upon matching of geometrical and shape features. Rid of the needs for pegs, the acquisition of the hand images is simplified and more user-friendly. Geometrical significant landmarks are extracted from the segmented images, and are used for hand alignment and the construction of recognition features. The recognition process is hierarchical in nature, and it employs a Gaussian Mixture Model for the first group of features, followed by a distance metric classification of the second group of features if necessary. The method has been tested on a medium size dataset of 323 images with promising results of 89% hit (true acceptance) rate and 2.2% false acceptance rate.