Ordered and fixed‐length bit‐string fingerprint representation with minutia vicinity combined feature and spectral clustering

Abstract

The minutiae set defined by the ISO/IEC 19794-2 is one of the prevalent feature used in fingerprint recognition systems. Unfortunately, such characteristic of unordered and variable-sized minutiae information causes a restriction on the operation in some advanced template protection methods (e.g. fuzzy commitment), which usually require an ordered and fixed-length binary feature representation as the system input. In this study, in order to simultaneously extend the application of fingerprint recognition and provide satisfactory system performance, the authors propose a novel fixed-length bit-string conversion framework based on spectral clustering and the proposed newly designed discriminative fingerprint representation called minutia vicinity combined feature (MVCF). The proposed method consists of three stages: (i) the extraction of MVCF, (ii) bit conversion via the spectral clustering algorithm, and (iii) matching. Benefiting from feature invariance, fixed-length and bit-oriented coding, merits such as fast matching and decent accuracy are well guaranteed. The performance evaluation is conducted on six publicly available benchmark data sets: FVC2002 DB1, DB2, DB3 and FVC2004 DB1, DB2, DB3 confirms the superiority of the proposed method and suggests the promise of migrating to some other domains (e.g., template protection).