Enhanced fingerprint verification and identification using a Widrow cellular neural network

Abstract

One of the most important challenges of fingerprint identification is the extraction of relevant details against distributed complex features. The parallel processing capability and learnable filtering features of cellular neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. In this paper, we propose to utilize the Widrow learning algorithm with a cellular neural network to efficiently enhance fingerprint details during the enrollment part. To evaluate the performance of the verification-identification part, enhanced fingerprint images are introduced into the fringe-adjusted joint transform correlator architecture for verification of an unknown fingerprint from a database. Comparison between the original and enhanced fingerprint identification and verification results is provided through computer simulation.