Abstract

In this study we describe a unique approach for fingerprint identification that makes use of principal component analysis (PCA). The process of extracting features from a fingerprint picture using optimized principal component analysis is described here. Fingerprint enhancement feature extraction calculation of eigenfingers using principal component analysis and the application of eigenfingers to the classification of a fingerprint picture are the components that make up the suggested technique. Last but not least we evaluate the identification performance of the principle component analysis (PCA)-based fingerprint recognition system by comparing eight different distance measures across feature vectors.