Preprocessing And Minutiae Extraction Of Fingerprint Image

Because of uniqueness and invariability, the fingerprint identification is becoming one of the most popular personal authentication technologies, and many fingerprint products have been offered nowadays. However, most of the key methods wouldn't be publicized, while better performance is required. So more reliable and accurate automated fingerprint identification systems (AFIS) have been of great interest to researchers.This paper focuses on the study of the fingerprint image's preprocessing and feature extraction approach under the automated fingerprint identification systems, which is based on minutia features. In the procedure of enhancing gray-level fingerprint images, we improve the method to get ridge distance. This improved method makes ridge frequency calculated by ridge distance more accurate. So it also makes the Gabor filter based on ridge frequency more effective. In the procedure of eliminating noises in binary fingerprint images, we put forward a new condition of seed-cleaning. This condition reduces the time of eliminating holes of binary fingerprint images. In the aspect of feature extraction and post-processing, a new approach to eliminate false features is presented. The elimination algorithm of false features is based on structural information of all kinds of noises in thinned fingerprint images and the distributing disciplinarian of fingerprint features. In the noting information of features, we raise a new method. The method doesn't depend on the existence of the center of fingerprint in images and uses the ridge count as the distance between two features.Author practices all algorithms mentioned in this paper with Visual C++ on the PIII computer. The experiment results demonstrate that new methods can enhance processing effects of fingerprint images. This paper has both theoretical and practical values.