Weighted multi-scale composite window fingerprint orientation field building method and hierarchical smoothing algorithm

Abstract

The invention discloses a weighted multi-scale composite window fingerprint orientation field building method and a hierarchical smoothing algorithm. The weighted multi-scale composite window fingerprint orientation field building method includes the following steps: first, building fingerprint orientation field OF information under a series of composite windows of different scales; then, determining the weight of each scale in the series according to square gradient consistency indexes; and finally, integrating the OF information under the whole series according to the weights to get a fingerprint orientation field OF. The fingerprint orientation field (OF) of a low-quality fingerprint image is reconstructed, and the orientation of a singular point area is better fitted. The orientation of a noise area can be estimated accurately, and position offset of a singular point is avoided. In addition, the fingerprint orientation field is processed using the hierarchical smoothing algorithm, which helps to repair a wrong ridge structure, and ensures that the position of a real singular point does not shift.

Description

technical field [0001] The invention belongs to the technical field of numerical image processing, and in particular relates to a method for establishing a weighted multi-scale composite window fingerprint direction field. Background technique [0002] Fingerprint Orientation Field (OF) shows the overall structure information of fingerprint ridges, and describes the basic shape, structural features and ridge information of fingerprints from a macroscopic perspective. Accurate and reliable OF has an important impact on many subsequent fingerprint image processing, such as fingerprint image enhancement, singular point detection, and fingerprint image classification. [0003] The gradient method to calculate fingerprint OF has the advantages of low computational complexity and intuitiveness, so it is one of the most widely used algorithms for calculating the fingerprint direction field, but it is also very susceptible to noise interference. In order to improve the OF calculati...

AI Technical Summary

Problems solved by technology

Subsequent researchers have improved this method to obtain a more accurate OF, but these methods are difficult to balance the accuracy and robustness issues.

Because the OF calculated by small blocks is more accurate but sensitive to noise, the OF calculated by large blocks is more resistant to noise but less accurate

Therefore, the researchers proposed the gradient method of the composite window. The experimental results show that the composite window can better balance the accuracy and robustness issues compared with the single window, but the composite window introduces a complex problem: how to choose the inner The size of windows and exterior windows? Existing methods rely on a large number of experiments to select more appropriate inner and outer window sizes based on experience

Unable to adaptively choose the size of the composite window

[0005] (2) How to accurately reconstruct the ridge infor

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