Method and system for finger vein anti-counterfeiting discriminating based on autoencoder

Abstract

The embodiment of the invention provides a method and a system for finger vein anti-counterfeiting discriminating based on an autoencoder, belonging to the field of biometric identification technology. The method comprises the steps of: establishing a training set according to collected finger vein images and corresponding tags; constructing a sparse autoencoder model and employing a gray-level image training set to perform training of a sparse autoencoder; employing the weight of the trained SAE (sparse autoencoder) to initialize a neural network, and performing training of the neural network; taking output of each hidden layer of the neural network as one extracted feature vector; and finally, respectively inputting each feature vector to one corresponding random forest classifier, employing a Bayesian model to perform decision fusion of output results, and achieving finger vein anti-counterfeiting discriminating.

Description

technical field [0001] The present invention relates to the technical field of biological feature identification, in particular to a finger vein anti-counterfeit identification method and system based on an autoencoder. Background technique [0002] The rapid development of information technology makes information security more and more important, which also means that higher and higher information security protection is required. As an emerging biometric technology, vein-based biometrics have attracted widespread attention in the field of biometrics. Compared with traditional biometric technologies, such as fingerprints, palm prints, iris, and face recognition, vein recognition technology has the advantages of low cost, easy data collection and non-contact operation. In addition, because the veins are located inside the living body, they are difficult to steal and counterfeit, and are less susceptible to changes in the surface skin, so they have higher security performance...

AI Technical Summary

Problems solved by technology

There are many finger vein anti-counterfeiting identification algorithms, such as Binarized Statistical Image Feature (BSIF), Rice Transform (RT), Local Binary Pattern (LBP), Local Phase Quantization (LPQ), but they use manual descriptor pairs Therefore, there are the following disadvantages in the prior art: (1) It is difficult to prove that the manually extracted features must be related to the authenticity of the finger vein image

(2) Even if there are features to distinguish the authenticity of the image, it is difficult to establish an effective mathematical model to describe them

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