Multi-acquisition-instrument fingerprint crossing-matching method based on size scaling estimation

Abstract

The invention discloses a multi-acquisition-instrument fingerprint crossing-matching method based on size scaling estimation. The method comprises the following steps that: A, a direction field of fingerprint is roughly calculated; direction values in a broken region are corrected; and the fingerprint according to the corrected direction field is enhanced; B, a method from thickness to thinness is adopted to estimate a size scaling value between template fingerprint and input fingerprint; C, a registration method based on detail-point clustering is adopted to determine candidate values of registration parameters, and the corresponding relation between detail-point sequences of the input fingerprint and the template fingerprint is established; and D, on the basis of all corresponding detail points, to similarity fractions for calculating normalization, multi-acquisition-instrument fingerprint crossing-matching based on size scaling estimation is realized. The method has the advantages of effectively and robustly processing the crossing-matching of multi-acquisition-instrument fingerprints, improving the interoperability of a matching method and realizing the robust matching of the multi-acquisition-instrument fingerprints, can be applied to the system with the coexistence of a plurality of acquisition instruments, and has important application value in the field of fingerprint recognition.

Description

technical field [0001] The present invention relates to technical fields such as biometric feature recognition image processing, pattern recognition, computer technology, etc., and in particular to a method for cross-matching fingerprints between multi-collectors based on size scaling estimation, which uses a coarse-to-fine method to estimate the size scaling value And use minutiae clustering to optimize global registration parameters. Background technique [0002] With the development of sensing technology, a variety of collectors coexist in the biometric market, such as optical collectors, capacitive collectors, thermal energy collectors, etc., which pose new challenges to fingerprint recognition algorithms. If different collectors are used in user registration and feature extraction, it will have a great impact on the characteristics of the original fingerprint data, the template structure and even the algorithm recognition results. Most of the existing fingerprint recog...

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Problems solved by technology

[0011] First of all, they construct a transformation space including size scaling, translation, and rotation. Therefore, as a dimension in the space, scaling must depend on other parameters for estimation, resulting in the performance of the estimation heavily dependent on the effectiveness of the search method.

[0012] Secondly, they all use the similarity between the calculated two-column point features (detail points) as the cost function during optimization, while ignoring other feature information of the fingerprint, such as ridges, etc., which limits the space for performance improvement

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