Classification of fingerprint images into individual classes using Neural Networks

In this paper, we propose a classification system for fingerprint images that is based on the number of registered fingerprint persons. Most automated fingerprint identification systems use prior classification of fingerprint for improvement of efficiency verification using minutiae as features. However, methods that use fingerprint minutiae needs improvement because they are limited to the number of classable data. Therefore, many fingerprints are classified together, consequently taking a long time to match and verify a given fingerprint. In this work, we propose a system that classifies fingerprint patterns into individual classes. Instead of the classification using minutiae, we propose a classification system that is based on individual features and the number of registered persons. Efficiency verification improves because we donpsilat need to compare an input fingerprint image to all registered fingerprint images using this system. The proposed system carries out classification using neural network.