Generating Fixed-Length Representation From Minutiae Using Kernel Methods for Fingerprint Authentication
提出一种基于核学习的点集到字符串转换框架，将指纹细节点转换为固定长度二进制串，实现快速匹配，在FVC2002和FVC2004数据集上验证了有效性。
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 competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness.