Finger vein recognition method and system based on convolutional variational autoencoder network

Abstract

The invention discloses a finger vein recognition method and system based on a convolutional variational autoencoder neural network. The system includes an image acquisition module, an image preprocessing module, an image feature extraction module, an image training module and an image recognition module; The method includes acquiring a finger vein image of a user to be identified; performing image preprocessing on the finger vein image information to extract a finger vein region of interest (ROI) image; extracting the finger vein ROI through a convolutional variational self-encoder neural network The finger vein feature code in the method; the feature code is input into a fully connected network for identification processing to identify the identity information of the user to be identified. The invention can effectively extract finger vein features, improve noise redundancy, and obviously improve recognition accuracy of a finger vein recognition system.

Description

technical field [0001] The invention relates to a biological feature identification method, in particular to a finger vein identification method and system based on a convolutional variational autoencoder neural network. Background technique [0002] Biometric technology is a technology for human identity authentication through human biological characteristics or behavioral characteristics. Among them, behavioral characteristics include gait, voice, signature, etc. Human biological characteristics mainly include two categories, external biological characteristics: such as face shape, palm shape, fingerprints, iris, etc.; internal biological characteristics: such as palm veins, finger veins and dorsal hand veins Wait. At present, common identification methods such as fingerprints, voice, signatures, faces, etc. are easy to forge and fragile, and the detection methods of iris, DNA, etc. are complicated and unfriendly. [0003] Finger vein recognition is an identification tec...

AI Technical Summary

Problems solved by technology

However, in the process of finger vein recognition, the traditional finger vein image recognition method is affected by many factors, and has the following defects in practical application: 1) it is easily affected by the environment in the finger vein image acquisition, in many cases such as ambient light, Ambient temperature and light scattering will affect the recognition rate; 2) vein images are susceptible to noise interference and affect the distribution of vein features in the image; 3) usually, it is difficult to establish an effective mathematical model to extract the distribution of each distribution feature

Therefore, the current methods based on manual features are difficult to effectively extract finger vein pattern information, resulting in limited recognition performance of the authentication system.

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