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A finger vein image anti-counterfeiting identification method and device

A finger vein and image technology, which is applied in the field of finger vein image anti-counterfeiting identification method and device, can solve the problem of low accuracy of finger vein image anti-counterfeiting identification, and achieve the effect of improving identification performance and improving accuracy

Pending Publication Date: 2018-04-10
CHONGQING TECH & BUSINESS UNIV
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Problems solved by technology

At present, there are many anti-counterfeiting identification algorithms for finger veins, such as binary statistical image feature (BSIF), Rice transform (RT), local binary pattern (LBP), local phase quantization (LPQ), and PAD algorithm, but they are not effective for finger veins. The accuracy of vein image anti-counterfeiting identification is relatively low

Method used

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  • A finger vein image anti-counterfeiting identification method and device
  • A finger vein image anti-counterfeiting identification method and device
  • A finger vein image anti-counterfeiting identification method and device

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Experimental program
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Effect test

Embodiment

[0143] (1) The collection and labeling of finger vein images are as follows:

[0144] The images in the sample set are from the "Spoofing-Attack Finger Vein Database" (https: / / www.idiap.ch / dataset / fvspoofingattack) of the Idiap Institute in Switzerland. The real and fake finger vein images in this database come from 440 images generated by 110 people, and the left and right index fingers of each person are collected twice, resulting in a total of 880 vein images. This is divided into three parts, namely, training set, verification set, and test set. Among them, there are 120 real and fake finger vein images in the training set, a total of 240, and there are 120 real and fake finger vein images in the verification set, a total of 240. The collection contains a total of 400 real and fake finger vein images, each with a size of 150*565 pixels. The training set is used to train the DBN model and the BP neural network model, the verification set is used for threshold estimation, a...

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Abstract

The invention provides a finger vein image anti-counterfeiting identification method and device. The method comprises the steps of: S1, collecting finger vein images and performing true and false marking on the collected images; S2; dividing the marked true and false images into a training set, a verification set and a testing set, wherein the images in the training set are obtained by performingpartitioning operation on the marked images; S3, performing DBN model building and training; S4, performing BP model building and training; S5, extracting the characteristics of all hidden layers in the BP model; S6, inputting the extracted characteristics of all the hidden layers, as multi-layer feature vectors of the input images, into an SVM model for training and determining SVM parameters; S7, fusing true and false scores of all the blocks of each image to calculate an overall score and judging whether a finger vein image is true for false via the overall score. The method and the devicecan effectively improve the accuracy of finger vein image anti-counterfeiting identification and improve the security performance of certification systems.

Description

technical field [0001] The invention relates to the technical field of biological feature identification, in particular to a method and device for anti-counterfeit identification of finger vein images. Background technique [0002] With the widespread application of Internet technology and the increasing rampant of terrorist activities, how to effectively identify identities to protect personal and property safety has become an urgent problem to be solved. Compared with traditional authentication methods such as keys and passwords, biometrics based on physiology and behavior are difficult to be stolen, copied and lost. Therefore, biometric authentication technology has been extensively studied and successfully applied to personal identity authentication. Biometrics currently used for identity authentication are mainly divided into two types: (1) external features: face, fingerprint and iris. (2) Internal features: finger veins, palm veins and dorsal hand veins. Intrinsic ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/10G06V40/14G06N3/048G06F18/214
Inventor 秦华锋刘霞
Owner CHONGQING TECH & BUSINESS UNIV
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