Automatic Fingerprint Verification using neural networks

Abstract

This paper presents an application of Learning Vector Quantization (LVQ) neural network (NN) to Automatic Fingerprint Verification (AFV). The new approach is based on both local (minutiae) and global image features (shape signatures). The matched minutiae are used as reference axis for generating shape signatures which are then digitized to form a feature vector describing the fingerprint. A LVQ NN is trained to match the fingerprints using the difference of a pair of feature vectors. The results show that the integrated system significantly outperforms the minutiae-based system alone in terms of classification accuracy. It also confirms the ability of the trained NN to have consistent performance on unseen databases. © Springer-Verlag Berlin Heidelberg 2002.