Fuzzy strong box remote identity authentication method based on face feature

Abstract

The invention discloses a fuzzy strong box remote identity authentication method based on a face feature. The method comprises: fuzzy strong box data is generated according to a human feature and a user password, wherein the fuzzy strong box data comprises a human feature value and key information; the fuzzy strong box data is stored into a server; a client obtains a user face image and a password when a user registers, and encrypts the obtained face feature to generate fuzzy strong box data, and sends the data to a database server; the database server obtains a user registration key and a user login key by computing the fuzzy strong box data, and compares the user login key with the registration key; if the user login key is the same as the registration key, the database server regards that the user can log in the system; and the user can select different passwords to protect the human feature, therefore, the cross-database search can be effectively prevented, the safety and privacy of the method can be effectively guaranteed, and the fuzzy strong box remote identity authentication method has application value.

Description

technical field [0001] The invention relates to an encrypted face-based real-name identity authentication method and system, belonging to the field of information security. Background technique [0002] With the development of social networking and informatization, people are faced with a very common problem, that is how to conduct identity authentication more quickly, conveniently and safely. Identity authentication occupies an extremely important position in the security system, and is the most basic security service, and other security services must depend on it. Once the identity authentication system is breached, all other security measures of the system will be useless. The target of hacker attacks is often the identity authentication system. Therefore, in order to speed up the construction of information security, it is even more important to strengthen the research on identity authentication theory and its application. In addition, after logging in to the system, ...

AI Technical Summary

Problems solved by technology

[0013] 1) When the confirmation is successful, the original biometric template is exposed, which is very dangerous;

[0014] 2) The method does not have the aforementioned diversity and revocability features;

[0015] 3) The non-uniform distribution characteristics of the original biological characteristics may reduce the security of the method

Relatively speaking, face features are generally ordered features, and the above scheme does not make full use of the order of face features;

[0020] 4) The intra-class variation of face features is large, and the quantization series with a certain step size is difficult to tolerate intra-class changes; many face features such as PCA, LDA, etc. are composed of some scalars distributed within a certain range. Therefore, the uniqueness between the components of the feature is not good, and it is easy to cross-match between the components

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