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Iris image quality evaluation method and system based on deep neural network

A deep neural network and iris image technology, which is applied in the field of iris image quality evaluation method and evaluation system based on deep neural network, can solve the problems that have not yet been published in patent documents and the difficulty of image restoration

Active Publication Date: 2020-10-20
上海点与面智能科技有限公司
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Problems solved by technology

However, iris image quality assessment realizes the identification and analysis of naturally formed low-quality images, and image restoration is more difficult.
At this stage, there are few relevant research results on the application of deep learning technology to iris image quality assessment, and no relevant patent literature has been published

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  • Iris image quality evaluation method and system based on deep neural network
  • Iris image quality evaluation method and system based on deep neural network
  • Iris image quality evaluation method and system based on deep neural network

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Embodiment Construction

[0066] The present invention will be further described in detail in conjunction with the following specific embodiments and accompanying drawings. The process, conditions, experimental methods, etc. for implementing the present invention, except for the content specifically mentioned below, are common knowledge and common knowledge in this field, and the present invention has no special limitation content.

[0067] The iris image quality evaluation method based on deep neural network in the present embodiment mainly comprises the following steps:

[0068] (1) Establishment of sample database;

[0069] (2) iris image preprocessing;

[0070] (3) Construction of multi-layer deep convolutional neural network model;

[0071] (4) Training of deep convolutional neural network model and determination of optimal model;

[0072] (5) Iris image test evaluation; that is, the optimal model based on deep learning is used to test and evaluate the iris image quality, and output the results...

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Abstract

The invention discloses an iris image quality evaluation method based on a deep neural network. The method comprises the following steps: establishing an iris image sample database; preprocessing theiris image; constructing a multi-layer deep convolutional neural network model; and training a multi-layer deep convolutional neural network model, and determining an optimal model; iris image test assessment. According to the method, real-time discrimination of blurred and clear iris images can be realized. According to the method, low-quality iris images generated according to different conditions have high recognition rate, good environmental adaptability, high automation degree and high operation speed, and iris images which do not conform to quality specifications can be rejected at an image acquisition end in real time. The invention further discloses an iris image quality evaluation system based on the deep neural network.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to an iris image quality evaluation method and evaluation system based on a deep neural network. Background technique [0002] With the development of information technology and people's growing need for security, identification technology based on biometrics has developed rapidly in recent years. As one of the important identity features, iris technology has three characteristics: non-contact image acquisition; uniqueness and stability of iris texture; and anti-counterfeiting for live detection. Iris recognition can make up for the limitations of other biometrics such as fingerprints and facial features in large-scale identification, so it is recognized as the most accurate biometric recognition technology at present. [0003] Iris image quality assessment is one of the key steps in iris recognition. Because it is widely used in the security field, iris acquisition equipment will...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/03G06K9/00
CPCG06T7/0002G06T2207/20081G06T2207/20084G06V40/18G06V10/993
Inventor 程治国
Owner 上海点与面智能科技有限公司
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