Automatic fingerprint distinguishing system and method based on template learning

Abstract

The automatic fingerprint distinguishing method based on template learning includes the following steps: registering fingerprint figure and recording the fingerprint template and characteristic information of the fingerprint figure to be matched; distinguishing fingerprint figure to judge whether the characteristic information of the fingerprint figure is the same as that of some template information in the system data base; feeding back detail information in fingerprint figure via knowledge rule to perfect corresponding reliable detail points in templates. The present invention classifies detail points of fingerprint templates, records the kernel of classes, interval and other information and has one coarse matching link before common matching. The present invention can extract and repair the characteristic information of multiple templates, raise the distinction effect and performance of automatic fingerprint distinguishing system, and has important application in biological distinction.

Description

technical field [0001] The invention belongs to the field of biometric feature identification, in particular to realizing the matching process of multi-template fingerprint images by cluster analysis and knowledge-based methods. Background technique [0002] In the 1990s, fingerprint identification technology gradually became a mature biometric identification method, which belongs to the field of "pattern recognition". First, input the extracted fingerprints into the computer, and then through a series of complex fingerprint recognition algorithms, anyone can be identified and authenticated based on the fingerprints in a very short period of time. At present, automatic fingerprint identification systems are widely used in fields that require identification. The application of automatic fingerprint identification is no longer limited to the field of law and public security. It can be used as a means for computers to confirm users, as an information security technology for ac...

AI Technical Summary

Problems solved by technology

When the effective area of ​​the fingerprint template collected is small, the number of detail points of the fingerprint template is less (for example: the collector of Authentic company can only collect the image of 128 * 128 pixels, and the fingerprint image collected can only be a part of the actual fingerprint, like this The extracted minutiae points are few and difficult to match); when the quality of the fingerprint image is poor, the extracted minutiae may have false minutiae such as position and direction.

Then in the above two cases, the methods they adopt are prone to misrecognition or refusal.

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