GMM-SVM Fingerprint Verification Based on Minutiae Only

Abstract

Most fingerprint recognition systems use minutiae information, which is an unordered collection of minutiae locations and orientations. Template protection algorithms such as fuzzy commitment and other modern cryptographic alternatives based on homomorphic encryption require a fixed size binary template. However, such a template is not directly applicable to fingerprint minutiae representation which by its nature is of variable size. In this study, we introduce a novel method to represent a minutiae set with a rotation invariant fixed-length vector. We represent each minutia according to its geometric relation with neighbors and use Gaussian mixture model (GMM) to model its feature distribution. A two-class linear SVM is used to create a model template for the enrollment fingerprint sample, which discriminates impressions of the same finger from other fingers. We evaluated the verification performance of our method on the FVC2002DB1 database.