Fingerprint Verification Using the Center of Mass and Learning Vector Quantization

This paper describes a new implementation of a mixture of techniques not used before for fingerprint recognition. The implementation consists of three stages: the location of the core, which is done through Radon transformation, the extraction of features (out of which a square fingerprint is produced with the core, and the center of the mass is obtained from it), in stage three, the resulting image is used to train the neural network in order to obtain better LVQ classification. The improvement of effectiveness is tested using two databases of fingerprints. Correct recognition rates have exceeded 90 percent, which demonstrate its great stability with fingerprints that display a well-defined core.