Learning type intellectual fingerprint identification comparison method adjusted based on fingerprint data quantity

Abstract

The invention discloses a learning-type intelligent fingerprint identifying and matching method which is adjusted through the quantity of the fingerprint data, and the method comprises the steps that original application fingerprints are stored into a memory after being carried out with data processing through a collector and a program, so as to compose a fingerprint template database. The invention is characterized in that the fingerprint template data is automatically upgraded and adjusted after the application fingerprints are successfully identified and matched, and the automatic upgrading and adjusting of the fingerprint template data refers to the steps that the application fingerprints after being successfully matched are input into the fingerprint template database, and are compared with one fingerprint or a plurality of fingerprints which accord with a certain data range in the fingerprint template database for analyzing, so as to obtain a fingerprint template data map used for the application fingerprint matching in future. The invention changes the traditional fixed matching template fingerprint database into a learning-type intelligent and dynamic fingerprint template database, so as to reduce and solve the condition that the application fingerprints of a person are unsuccessfully matched with the fingerprint template database because the application fingerprints are changed, thereby greatly improving the practicability of the fingerprint technique.

Description

technical field [0001] The invention relates to a fingerprint identification method, in particular to a learning type intelligent fingerprint identification and comparison method through adjusting the quantity of fingerprint data. Background technique [0002] The current fingerprint collection and identification comparison method is to directly compare the application fingerprint with the fixed template fingerprint database. The fingerprint template database will not be automatically updated after the first reading, so when the application fingerprint of the human body changes. It is impossible to change or update the corresponding database according to the changed fingerprint, which will lead to the situation that the application fingerprint cannot be successfully compared with the template fingerprint database. Let us first introduce the traditional fingerprint collection, storage, comparison methods and their shortcomings as shown in Figure 1: [0003] 1. We analyze the...

AI Technical Summary

Problems solved by technology

If it is not within the set value range, the comparison will fail

[0015] 3. The traditional fingerprint identification and comparison method only compares the application fingerprint with the fingerprint template database stored in the memory after data processing through the collector and program. If the comparison fails, the application fingerprint is required to be compared again. If the comparison is successful Then give the application fingerprint a certain program setting authorization, and the whole identification and comparison process ends here, so it has the disadvantages of:

[0018] 3) In the traditional fingerprint collection, storage and comparison mode, since the fingerprint comparison database template is fixed, when the fingerprint or fingerprint feature value changes, when the same application fingerprint is compared with the template fingerprint database There may be a comparison failure

The first solution requires manual addition or modification of the fingerprint template database, which is very inconvenient

The second way is to wait for the application fingerprint to change back to the fingerprint in the fingerprint template database. Either it needs a slow process, or it cannot be changed back, causing the comparison to continue to fail.

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