Identity identification method based on cell neural network self associative memory model

Abstract

The invention discloses an identity identification method based on a cell neural network self associative memory model. The method includes the steps of acquiring m fingerprint pictures and face pictures, grouping and numbering the pictures, setting the binary image luminance threshold for the fingerprint picture groups and face picture groups, obtaining a binary fingerprint picture set and a binary face picture set, establishing a fingerprint association memory input matrix and output matrix and a face association memory input matrix and output matrix, establishing a cell neural network fingerprint picture identification model with unknown fingerprint model parameters and a cell neural network face picture identification model with unknown face model parameters, determining the cell neural network fingerprint picture identification model, determining the cell neural network face picture identification model, and conducting identification and matching. Trough a data form, leakage during a transmission process is impossible, and the safety is high. The storage volume is small, and the identity identification effect is good.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an identity recognition method based on a cellular neural network self-associative memory model. Background technique [0002] With the development of the big data era, people have identity information checks during travel, such as face recognition. Through identity recognition, identity verification is realized, the security performance of the system is improved, and different user identity information is confirmed. [0003] When checking the face information, it must include the face information saved in the database and the face information waiting for verification. For the face information saved in the database, in the prior art, there are the following defects: [0004] First: face information is often directly stored, which makes identity information easy to leak and has a low safety factor. Once leaked, it is easy to be copied and has poor reliability. [0005]...

AI Technical Summary

Problems solved by technology

[0004] First: Face information is often stored directly, which makes identity information easy to leak and has a low safety factor. Once leaked, it is easy to be copied and has poor reliability.

[0005] Second: In the process of face image recognition, it is necessary to retrieve and compare a large amount of image data in the database, and it takes a long time to retrieve images, resulting in low recognition efficiency and prone to errors

[0006] Third: In the process of image recognition, when similar images are found, there is no image matching, the reliability is poor, and verification errors are more likely to occur

[0007] Fourth: Only a single identity information is verified, and the verification result is unreliable

Images