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

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...

AI Technical Summary

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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