A palmprint recognition method based on unet++ network

A palmprint recognition and network technology, applied in the field of biometric recognition, can solve the problems of high cost and difficult model training, and achieve the effect of improving efficiency and shortening time.

Active Publication Date: 2021-05-25
北京智能工场科技有限公司
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method model is difficult to train and requires a large number of palm-labeled pictures
At present, there is no publicly marked palmprint data, and all training data needs to be marked manually, so this method is relatively expensive

Method used

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  • A palmprint recognition method based on unet++ network
  • A palmprint recognition method based on unet++ network
  • A palmprint recognition method based on unet++ network

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Embodiment Construction

[0045] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. In the following, the invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0046] First, several concepts used in the present invention are introduced.

[0047] Deep learning: deep learning is to learn the internal laws and representation levels of sample data. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans, and to be able to recognize data such as text, images, and sounds.

[0048] Convolutional Neural Network: Convolutional Neural Networks (CNN) is a type of feedforward neural network ...

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Abstract

The present invention designs and implements a palmprint recognition method based on UNet++ network. The method includes constructing training set and test set samples, constructing a UNet++ network model, and using the constructed training set and test set samples to identify the UNet++ network. The model is trained and the palmprint is recognized by using the trained UNet++ network model. The technical scheme of the present invention converts the regression problem of the corresponding coordinates of different types of palm lines into the pixel-by-pixel classification problem of different palm line categories, which not only simplifies the training difficulty of the model but also improves the accuracy of palm line prediction. At the same time, with the help of the final pruning structure of the UNet++ network model, the model prediction efficiency can be greatly improved. The present invention also discloses a computer-readable storage medium for implementing the above method.

Description

technical field [0001] The invention relates to the technical field of biological feature identification, in particular to a UNet++ network-based palmprint identification method and a computer-readable storage medium for realizing the method. Background technique [0002] Traditional palmprint recognition has the defects of subjective assumptions and propagandizing feudal superstition. Through the current image recognition technology and big data analysis capabilities, it is possible to realize the technical analysis and statistics of a large number of users' palmistry, and to make statistics on the accuracy of their descriptions. To achieve the effect of removing the dross and extracting the essence. [0003] The Chinese invention patent application with the application number CN201911045739.3 proposes a palmprint verification method, device, computer equipment and readable storage medium. The method includes: when a palmprint verification instruction is received, the image...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/1347G06V40/1365G06N3/045G06F18/241G06F18/253G06F18/214
Inventor李玲贺同路杨菲李嘉懿郭学栋任永亮
Owner北京智能工场科技有限公司