Iris recognition neural network model training method based on binocular fusion

A neural network model and training method technology, applied in the field of iris recognition neural network model training based on binocular fusion, can solve problems such as difficulty in achieving the optimal effect, achieve improved feature expression ability, improve accuracy, and be more discriminative and The effect of robustness

Active Publication Date: 2020-06-12
天津中科智能识别有限公司
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Problems solved by technology

However, such a fusion strategy without parameter optimization is usually difficult to achieve the optimal effect.

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  • Iris recognition neural network model training method based on binocular fusion

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Embodiment Construction

[0015] 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.

[0016] 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.

[0017] 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 an iris recognition neural network model training method based on binocular fusion. In the training process of the deep neural network model for iris recognition, the binoculariris of the same individual is input into the recognition model for training, complementary information of the binocular iris is considered, joint learning and optimization of binocular iris featuresare realized, and the binocular fusion features with higher discrimination and robustness are obtained through the training mode, so that the performance of the recognition model is improved. According to the method for performing deep neural network training by fusing the binocular information provided by the invention, the problem that the optimization target in the training process is inconsistent with the actual demand can be solved, and the complementary information of the iris regions of the two eyes can be fully extracted. Two eyes are fused for training, the feature expression capability of the model can be effectively improved, and the extracted iris features are more discriminative and robust, so that the iris recognition accuracy is significantly improved.

Description

technical field [0001] The invention relates to the technical field of identification, in particular to a method for training an iris recognition neural network model based on binocular fusion. Background technique [0002] Iris recognition is a biometric identification method with high reliability and high security. Compared with other biometric identification modes such as face and fingerprint, it has the advantages of uniqueness, stability, and anti-counterfeiting, and has been widely used in identity authentication scenarios such as security, finance, and border inspection. [0003] Iris feature extraction is the core key step of iris recognition system. In recent years, the feature extraction method based on the deep neural network model has gradually become the research hotspot and mainstream method of iris feature extraction, and the training method and strategy of the deep neural network model are the most critical links that determine the performance of this type o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V40/193G06N3/045
Inventor 孙哲南谭铁牛任民王云龙骆正权
Owner 天津中科智能识别有限公司
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