Iris coding method based on novel normalization and deep neural network

A technology of deep neural network and coding method, which is applied in the field of iris coding based on new normalization and deep neural network, can solve the problem of large amount of network calculation, and achieve the effect of reducing storage capacity, improving recognition effect, and improving recognition speed.

Pending Publication Date: 2021-12-24
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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But, the network calculation amount designed by CN111027464A is bigger

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  • Iris coding method based on novel normalization and deep neural network
  • Iris coding method based on novel normalization and deep neural network
  • Iris coding method based on novel normalization and deep neural network

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[0026] In order to make the technical means, creative features, goals and effects of the present invention easy to understand, the following is a detailed description of the iris coding method based on the new normalization and deep neural network of the present invention in conjunction with the embodiments and accompanying drawings.

[0027]

[0028] figure 1 It is a flow chart of the iris encoding method based on novel normalization and deep neural network in the embodiment of the present invention.

[0029] Such as figure 1 As shown, in step S1, the iris image data is collected and marked.

[0030] figure 2 It is a schematic diagram of iris image data labeling in the embodiment of the present invention.

[0031] Such as figure 2 As shown in FIG. 2 , class numbering is carried out on the collected iris images, and the iris images from the same eye are classified into one class, and the class numbering is carried out on the collected iris images. The marked label is ...

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Abstract

The invention provides an iris coding method based on novel normalization and a deep neural network, which is used for coding iris image data, and is characterized by comprising the following steps: S1, collecting the iris image data; s2, performing data amplification on the iris image data; s3, performing normalization processing on the iris image subjected to data amplification; s4, building an iris coding network model and training the iris coding network model to obtain an optimal iris coding network model; and S5, inputting the iris sample into the model to obtain an iris coding feature vector. The invention provides a biological feature recognition method. The method comprises the following steps: S6, carrying out the statistics of matching pair similarity scores according to the iris coding feature vectors corresponding to all to-be-recognized images, and calculating an optimal matching threshold value; and S7, performing matching based on the optimal matching threshold value by calculating the similarity score between the iris coding feature vectors of the to-be-recognized image, thereby performing identity confirmation.

Description

technical field [0001] The invention relates to an iris encoding method based on novel normalization and deep neural network. Background technique [0002] On the issue of human identity authentication, traditional identity verification methods are inconvenient and insecure, while biometric technology based on biological characteristics has the characteristics of universality, stability, and security, which makes biometric technology an important field in this field. research hotspot in recent years. Among them, iris recognition is considered to be the most promising biometric identification method. Compared with other biometric technologies, iris recognition has the advantages of uniqueness, stability, anti-counterfeiting and non-contact. [0003] A complete iris recognition system consists of four parts: iris image acquisition, iris preprocessing, iris feature coding and iris feature matching, among which feature coding is the key link of iris recognition. Feature coding...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/22G06F18/214
Inventor 沈文忠贾丁丁
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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