Biometric Identification through Hand Geometry
A new approach for person identification based on hand geometry is presented. After preprocessing hand features are extracted from a photograph taken while user has placed his/her hand (either left or right) on the platform of a document scanner with no limits or fixation. Different pattern recognition techniques like Gaussian mixture modeling (GMM), radial basis function neural networks (RBF), multilayer perceptron (MLP), k-nearest neighbor (k-NN), Bayes method and Mahalanobis/Hamming distance have been used in classification section. Experimental results show a rate of success above 90%