Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations
By B. Topcu, Hakan Erdogan. 2019
11 citations recorded by the source corpus.
Abstract Fingerprint minutiae information is an unordered and variable-sized collection of minutiae locations and orientations. Advanced template protection algorithms which require a fixed-length binary template cannot operate on minutiae points. In this paper, we propose a novel framework that provides practical solutions that can be used in developing secure fingerprint verification systems. The framework, by using a GMM-SVM fingerprint representation scheme, first generates fixed-length feature vectors from minutiae point sets. The fixed-length representation enables the application of modern cryptographic alternatives based on homomorphic encryption to minutiae template protection. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching. The verification performance of the framework is evaluated on FVC2002DB1A and DB2A databases.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
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Citations in the corpus
- Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “One of the most outstanding application areas for the SVMs is biometrics [13], [14], [24]–[28].”