Method for evaluating quality of multi-scale gradual iris image

Abstract

The invention discloses a method for evaluating the quality of a multi-scale gradual iris image, comprising the following steps of firstly, obtaining the iris image through an iris collection device; secondly, carrying out pixel level image quality evaluation on the iris image, wherein the pixel level image quality evaluation comprises steps of illumination evaluation, light pot reflection evaluation, location reasonality evaluation, fuzzy evaluation algorithm and occlusion evaluation algorithm; thirdly, evaluating the quality of an image level image: finishing image level quality evaluation on the basis of a result of the pixel level image quality evaluation. By adopting methods of regionalization, weighing and multiple scales, the method implements gradual quality evaluation on various interference factors of the iris image on the aspects of a pixel level and the whole image, thereby avoiding influences of external interference factors such as illumination, occlusion, inaccurate focusing and the like in the process of iris collection on the iris image, further lowering the error recognition rate and the recognition rejection rate of an iris recognition system, and improving the efficiency and the accuracy rate of the iris recognition system.

Description

technical field [0001] The invention belongs to the technical field of biological feature recognition, and relates to a multi-scale progressive iris image quality evaluation method. Background technique [0002] Iris image quality assessment is an important part of the iris recognition system. By evaluating the quality of the collected iris images and selecting iris images that meet the requirements for iris feature recognition, the quality of sample images used for iris recognition can be improved. Quality, thereby reducing the false recognition rate and rejection rate of the iris recognition system. An excellent iris image quality assessment method can make the entire iris recognition have a lower rejection rate and false recognition rate, and thus have a higher recognition rate. [0003] In the existing automatic iris identification system, since the image is collected, it is non-invasive to the person being collected. The collected iris image usually includes not only ...

AI Technical Summary

Problems solved by technology

Eyelids, eyelashes, etc. often block the effective area of ​​the iris, the contrast caused by poor lighting is too small, the light spots in the eye image are caused by reflections during shooting, and the human eye moves or the camera equipment is not accurately focused during shooting. Blurring and other phenomena will affect the quality of the iris image and reduce the recognition accuracy of the iris image

[0004] At present, the preliminary research results of iris recognition technology are mainly based on the ideal environment of the laboratory. Compared with the related research results of iris positioning, iris feature extraction and iris feature matching, the research results of iris image quality evaluation are relatively few, and Mostly for the evaluation of a certain interference factor of the iris image, the evaluation is single

Although there are methods for evaluating a variety of interference factors, multiple evaluation methods are used, and they are independent of the iris system. The evaluation method is cumbersome and the calculation complexity is large, which is not suitable for engineering applications.

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