Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints

Representation of fingerprints is one of the key factors that limits the accuracy and efficiency of matching algorithms. Most popular methods represent each fingerprint as an unordered set of minutiae with variable cardinality and the matching algorithms are left with the task of finding the best correspondence between the two sets of minutiae. While this makes the representation more flexible and matching more accurate, the task becomes computationally intensive. Fixed length representations with aligned features are highly efficient to match. However, creating an aligned representation without the knowledge of the sample to which it is to be matched, makes the problem of representation more complex. Some of the fixed-length representations only provide partial alignment, leaving the rest to the matching stage. In this paper, we propose a fixed length representation for fingerprints that provides exact alignment between the features, thus enabling high-speed matching with minimal computational effort. The representation extends the idea of object representation using bag of words into a bag of minutiae neighborhoods. The representation is provably invariant to affine transformations (rotation, translation and uniform scaling), and is shown to be highly discriminative for the task of verification. Experimental results on FVC 2002 and 2004 datasets clearly show the superiority of the representation with competing methods. As the proposed representation can be computed from the standard minutiae templates, the method is applicable to existing datasets, where the original fingerprint images are not available.