Fingerprint image synthesis based on statistical feature models

Fingerprint image synthesis has received considerable attention because of its potential use in generating large databases to evaluate the performance of fingerprint recognition systems. Existing fingerprint synthesis algorithms (e.g., SFinGe) focus on rendering realistic fingerprint images, but the features (e.g., minutiae) in these fingerprints are formed in an uncontrollable manner. However, generating synthetic fingerprint images with specified features is more useful in developing, evaluating and optimizing fingerprint recognition systems by providing ground truth features in the synthesized images. In this paper, we propose a method to synthesize fingerprint images that retain prespecified features (i.e., singular points, orientation field, and minutiae). To obtain realistic fingerprints, these features are sampled from appropriate statistical models which are trained by using real fingerprints in public domain databases. We validate the proposed method by comparing the synthesized images with those generated by SFinGe and by investigating the match score distributions on synthesized and real fingerprint databases. Furthermore, the synthesized fingerprint images and their minutiae are used to evaluate the matching capabilities of two commercial off-the-shelf (COTS) fingerprint matchers.