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The ISO/IEC 19794-2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: (1) minutiae descriptor extraction; (2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; (3) a dynamic feature binarization; and (4) matching. The promising experimental results on six datasets from fingerprint verification …