Multiple linear regression associative memory model-based fingerprint and face coupling recognition method

Abstract

The invention discloses a multiple linear regression associative memory model-based fingerprint and face coupling recognition method. The method includes the following steps that: S1, a fingerprint picture and a face picture are collected; S2, the associative memory input matrixes and output matrixes of the fingerprint picture and face picture are obtained; S3, a multiple linear regression fingerprint picture recognition model with regression parameters and a multiple linear regression face picture recognition model with regression parameters are constructed; S4, the regression parameters are calculated, and the multiple linear regression fingerprint picture recognition model and the multiple linear regression face picture recognition model are obtained; and S5, the fingerprint picture and face picture are recognized. The multiple linear regression associative memory model-based fingerprint and face coupling recognition method can realize multiple recognition of identity information and has high reliability. According to the method, the associative memory and the multiple linear regression models are combined together, the pictures are converted into the parameters, and therefore, the method has the advantages of high safety factor, good recognition effect, good protection effect of the identity information and high confidentiality.

Description

technical field [0001] The invention relates to the technical field of image data preservation, in particular to a fingerprint and face coupling recognition method based on a multiple linear regression associative memory model. Background technique [0002] With the development of the big data era, people usually save their daily photos or even ID photos in the database, which is easy for hackers to steal private information for trafficking or criminal activities, so that people's private information is leaked, and daily Life is easily disturbed or even involved in criminal incidents, which can easily cause a lot of inconvenience. [0003] In some institutions that need to verify identity information, people's identity information is usually collected for identity verification. For example, collecting people's face information or fingerprint information. The amount of information is small, and the credibility of identity verification is not high. And when people's identit...

AI Technical Summary

Problems solved by technology

The amount of information is small, and the credibility of identity verification is not high

And when people's identity information is saved, it is usually saved directly, without processing and saving the photos, the safety factor is low, and the identity information is easy to be stolen

Once the identity information database is attacked by criminals, the identity information is extremely easy to be stolen to carry out illegal activities, causing unnecessary troubles to people

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