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Genetic automatic fuzzy clustering analysis-based templateless biological secret key generating method

A fuzzy clustering analysis and fuzzy clustering technology, applied in the field of biometrics, can solve the problems of leakage of privacy, illegal intrusion into the system, easy theft of biometric information, etc., to achieve broad application prospects and avoid harm.

Active Publication Date: 2014-02-12
ZHEJIANG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, storing biometric information as a template introduces many security issues, and there is a risk of privacy leakage
First, the stored biometric information is easily stolen and used for counterfeiting, thereby illegally hacking into the system

Method used

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  • Genetic automatic fuzzy clustering analysis-based templateless biological secret key generating method
  • Genetic automatic fuzzy clustering analysis-based templateless biological secret key generating method
  • Genetic automatic fuzzy clustering analysis-based templateless biological secret key generating method

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] The concrete implementation process of the present invention comprises the following steps:

[0035] Step 1: Obtain several biometric samples from the user, and calculate the statistical characteristic value of each biometric sample. Based on the calculated statistical eigenvalues ​​(that is, training data), the genetic automatic fuzzy clustering algorithm is used to perform fuzzy cluster analysis on the data.

[0036] Since data objects from the same user are usually similar, they tend to be clustered into the same cluster, especially when the stability of user feature components is high. Instead, data objects from different users tend to be clustered into different clusters. Therefore, clustering results can be used to model differences within and between groups.

[0037] Genetic Automatic Fuzzy Clustering Algorithm

[0038] Extract the training data set X...

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Abstract

A genetic automatic fuzzy clustering analysis-based templateless biological secret key generating method comprises the following steps of (1) acquiring a plurality of biological feature samples from a user, calculating the statistics feature value of each biological feature sample and applying a genetic automatic fuzzy clustering algorithm to perform fuzzy clustering analysis on the data; (2) calculating the average fuzzy membership of each feature assembly and each cluster by a fuzzy clustering result according to a fuzzy clustering result, determining the stability of the feature assemblies and measuring the stability of the feature assemblies by using the obtained membership; (3) selecting of the feature assembly: selecting feature assembly with higher stability to generate a secret key based on the stability result of the feature assembly obtained from the last step; (4) generating of the biological secret key: after the feature assembly of each user is well determined, for the clustering result on each feature assembly, marking each cluster with one secret key, determining the corresponding attribution cluster and secret position of each selected feature assembly, and generating the secret key for each user by all the key positions obtained by combining.

Description

technical field [0001] The invention relates to the technical field of biological identification, in particular to a method for generating a template-free biological key based on genetic automatic fuzzy clustering analysis, which is used for identity verification of a security system. Background technique [0002] Authentication is a key component of many security systems today, especially in the context of the widespread use of e-commerce, secure and reliable authentication methods are becoming increasingly important. [0003] Traditional identity verification methods mainly use objects owned by users (such as ID cards) or secret knowledge (such as passwords) to verify identities. Although these technologies have been widely used in many existing security systems, they have many disadvantages. For example, these systems cannot prevent people who illegally obtain verification objects or access passwords from accessing the system. Recently, people have started to adopt biome...

Claims

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Application Information

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IPC IPC(8): G06F21/32
CPCG06F21/32G06N3/126
Inventor 盛伟国白丽叶应豪超卢梦雅陈胜勇
Owner ZHEJIANG UNIV OF TECH
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