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Iris splitting method suitable for low-quality iris image in complex application context

A technology of application scenarios and iris images, applied in instruments, calculations, character and pattern recognition, etc., to achieve the effect of accurate and reliable iris outer circle outline, accurate related operations, and guaranteed accuracy

Inactive Publication Date: 2010-12-22
HEILONGJIANG UNIV
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  • Iris splitting method suitable for low-quality iris image in complex application context
  • Iris splitting method suitable for low-quality iris image in complex application context
  • Iris splitting method suitable for low-quality iris image in complex application context

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Embodiment 1

[0034] The present invention is an iris segmentation method adapted to low-quality iris images in complex application scenarios, using the human eye detector based on the AdaBoost algorithm to initially determine the human eye sub-image, and then further applying the image based on co-occurrence histogram and K-Means clustering Segmentation and elliptical Hough transform to determine the sub-image of the human eye more accurately, using the improved Hough transform to locate the outer contour of the iris and output the integral and differential operator value CID of the detection result, if the outer contour of the iris is not positioned accurately enough, the iris image is binary-valued To judge whether it is an eye-closed image, use the skin color information to re-determine the outer contour of the iris for the non-closed-eye image, and finally use a one-dimensional signal detection algorithm and a constrained parabolic integral-differential operator to detect the upper eyeli...

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Abstract

The invention discloses an iris splitting method suitable for low-quality iris images. The prior art can not carry out robust splitting to the low-quality iris image with mass interference and noise. The invention uses a human eye detection algorithm to preliminarily determine a subimage of the human eye and is applied to image splitting based on interdependent histogram and cluster and ellipse Hough conversion to accurately determine the subimage of the human eye; improved Hough conversion is adopted to position the outer contour of the iris and output the integrodifferential operator value CID of a detection result; if the outer contour of the iris is not accurately positioned, parabola approximation is carried out on the image to judge whether the image is an eye-closed image; for the non eye-closed image, complexion information is utilized to re-determine the outer contour of the iris; the palpebra superior is detected by one-dimensional signal detection and parabola integrodifferential operator; the palpebra inferior is detected by one-dimensional signal detection and an RANSAC algorithm; the histogram in the iris is calculated; and a threshold value is found to remove a highly bright spot. The invention is used for iris splitting of low-quality iris image in a complex application context.

Description

technical field [0001] The technical fields involved in the invention include image processing, pattern recognition and machine learning. Specifically, a method for iris segmentation for low-quality iris images in complex application scenarios is proposed. Background technique [0002] Iris-based biometric identification technology has the advantages of high recognition accuracy, no forgery, and non-invasiveness. It is widely used in the access control systems of residential and intelligent buildings, customs entry and exit, airports, financial, securities, insurance, and social welfare institutions. It has broad application prospects in the field. However, the current iris recognition system requires a high degree of cooperation from the user, can only be collected at a short distance and requires the user not to move, so the application of the iris recognition system is greatly restricted and cannot be applied to various complex application scenarios. [0003] In order to...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/00
Inventor 李培华刘晓敏
Owner HEILONGJIANG UNIV
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