A generative model for fingerprint minutiae
Fingerprint minutiae are the most important features used by latent fingerprint examiners, as well as in automated fingerprint recognition systems. Hence, understanding the statistical distribution of minutiae is essential in many fingerprint recognition related problems, such as fingerprint individuality and fingerprint synthesis. Prior work considers the occurrence of a minutia as a random event, and mostly assumes that individual minutiae are independent of each other. Some studies also considered the clustering tendency of minutiae and the minutiae neighborhood structures. Yet, it remains unclear whether the ridge orientation field has an impact on the minutiae occurrence. This paper investigates the correlation between ridge orientation field and minutiae. Assuming that minutiae are distributed conditionally on the variation in local ridge orientation, a new generative model is proposed for fingerprint minutiae. To evaluate the proposed model, we train the model using fingerprint images in the NIST SDl4 database, and simulate the minutiae in the fingerprints in the NIST SD4 database with the trained model. The experimental results show that by exploiting both the local ridge orientation variation and the neighborhood minutiae structure, the proposed model can better simulate the minutiae extracted from fingerprints than other models available in the literature.