Comparative study of learning algorithms for recognition by hand geometry

This paper presents an approach for personal recognition based on hand geometry applying different classification and training methods to measure the results. The features extraction process prioritizes user comfort during capture and produces segmentation of hands and fingers with high precision. For classification, Bayesian networks and support vector machines methods were applied in three different implementations. Tests using cross-validation and random subsampling techniques were performed. The experiments demonstrated competitive results when compared to other state-of-the-art methods, especially for classification using cross-validation applied to BayesNet and SMO classifiers, both with an accuracy of 99.85%.