Robust iris region segmentation method based on specific boundary detectors

Abstract

The invention discloses a robust iris region segmentation method based on specific boundary detectors, which comprises the following steps: S1, constructing a left and right inner boundary, left and right outer boundary and upper and lower eyelid boundary iris specific boundary training data set and a candidate feature set; S2, constructing the six iris specific boundary detectors by using a cascaded self-adaptive learning algorithm; S3, positioning an iris inner boundary by utilizing the left and right inner boundary detectors and weighted Hough transform; S4, positioning an iris outer boundary by utilizing the left and right outer boundary detectors and the weighted Hough transform; and S5, positioning an upper eyelid boundary by utilizing the upper and lower eyelid boundary detectors and a robust least square method. By utilizing the robust iris region segmentation method, an iris effective area can be accurately segmented in an iris image containing a lot of noise, so that the accuracy, the robustness and the usability of an iris identification system are improved. The robust iris region segmentation method can be widely applied to various application systems for identity recognition and security defense by using iris recognition.

Description

technical field [0001] The invention relates to the technical fields of digital image processing, pattern recognition, statistical learning and the like, in particular to a robust iris region segmentation method based on a specific boundary detector. Background technique [0002] Iris recognition is an identification method based on the texture characteristics of the iris of the human eye to determine a person's identity. It has the characteristics of high uniqueness, strong stability, and non-invasiveness. It has been successfully applied to identity identification in airports, customs, banks and other occasions. [0003] In the actual application of the iris recognition system, the iris image collected by the iris imaging device often contains many noisy areas, such as eyelids, eyelashes, mirror frames, and light spots. What is needed for iris recognition is only the iris area between the black pupil and the white sclera and not blocked by various noises, which is called t...

AI Technical Summary

Problems solved by technology

[0006] Although the above methods have achieved good segmentation results in some iris images containing noise, they still have some defects: First, they have unclear definitions of iris boundaries and noise edges, so the iris boundary points selected by specific logic rules are likely to be or noise edge points, and vice versa

Finally, in the long-distance iris recognition system, there are a large number of noisy regions in the iris image, which brings great challenges to iris region segmentation, making many originally effective segmentation methods incompetent.

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