Novel efficient iris image quality evaluation method based on deep neural network

A deep neural network and iris image technology, applied in the field of iris image quality evaluation, can solve the problems of inaccurate results and slow speed, defocus blur, time-consuming and high accuracy of iris image quality factor fusion method, and achieve fast calculation speed, Short time-consuming, high-accuracy results

Active Publication Date: 2020-06-26
天津中科智能识别有限公司
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Problems solved by technology

[0004] The present invention aims at iris images acquired under complex conditions such as long-distance and unrestricted users, and there are different degrees of degradation and interference factors such as defocus blur, motion blur, squint, pupil scaling, eyelid occlusion, iris size change, etc., resulting in D

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  • Novel efficient iris image quality evaluation method based on deep neural network
  • Novel efficient iris image quality evaluation method based on deep neural network
  • Novel efficient iris image quality evaluation method based on deep neural network

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[0023] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0024] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, it indicates There are features, steps, operations, parts or modules, components and / or combinations thereof.

[0025] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in ...

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Abstract

The invention discloses a novel efficient iris image quality evaluation method based on a deep neural network. A feature extraction model is used to extract a feature map of an iris image in an inputimage, then a reconstruction model is used to estimate an iris effective area thermodynamic diagram from the feature map of the iris image, finally a quality prediction model uses the iris effective area as an area of interest, and the overall quality score of the iris image is calculated from the feature map. According to the method, other processes such as preprocessing or segmentation and positioning do not need to be carried out on the collected eye image;, the deep neural network can be directly used for extracting the global features of the eye image, the thermodynamic diagram of the iris effective area is automatically estimated according to the extracted features, the global features of the iris and the thermodynamic diagram of the iris effective area are combined through a visualattention mechanism, and quality evaluation is carried out on the iris image. The iris image quality evaluation method provided by the invention is simple in process, high in calculation speed and high in robustness and adaptability.

Description

technical field [0001] The invention relates to the technical field of iris image quality evaluation, in particular to a new method for efficient iris image quality evaluation based on a deep neural network. Background technique [0002] As an efficient and stable biometric identification method, iris recognition has the advantages of high security, high stability, and good anti-counterfeiting performance compared with other biometric identification methods such as face and fingerprint. It has been widely used in customs clearance, Security, attendance, finance, social security and other scenarios that require precise identity authentication. The process of iris recognition generally includes image acquisition, iris segmentation and positioning, normalization, feature extraction, matching and other links. [0003] Iris image quality evaluation is an important step in iris image preprocessing. It generally refers to the calculation of iris image quality factors including but...

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Application Information

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IPC IPC(8): G06T7/00G06K9/32G06K9/62G06N3/04
CPCG06T7/0012G06T2207/30041G06T2207/30168G06V10/25G06N3/045G06F18/214Y02P90/30
Inventor 孙哲南王乐源张堃博王云龙
Owner 天津中科智能识别有限公司
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