Ellipse-detection-based iris detection method

Abstract

The invention discloses an ellipse-detection-based iris detection method. The method comprises: step one, obtaining an image including an eye region; and step two, carrying out ellipse detection on the eye region; to be specific, step 2-1, carrying out ellipse detection on the eye region by using an ELSD algorithm; to be specific, changing the sensitivity of the ELSD for N times to obtain information of N groups of circles; step 2-2, according to the obtained circle information, constructing a circumference by using a point set of circumferential points in the circle information; step 2-3, for each point of the point set, constructing feature vectors of the point and selecting feature parameters, including a circle center coordinate, a radius, an arc degree, an arc length, and a circumferential point coordinate, of the point to carry out weighted processing to obtain feature vectors; step 2-4, classifying the feature vectors, using one class with the largest individual number as a target class, and storing circumferential point coordinates of all feature vectors; and step 2-5, carrying out circle fitting by using the circumferential point coordinates in the step 2-4.

Description

technical field [0001] The invention relates to iris extraction technology, in particular to an iris detection method based on ellipse detection. Background technique [0002] The extraction technology of iris image has high application value. Accurate, robust, and fast eyeball image extraction is the premise and basis for the application of other computer vision technologies such as human-computer interaction, expression analysis, gaze control, and fatigue monitoring. [0003] The traditional iris extraction technology is generally based on the frontal face image, and is processed by edge extraction or threshold separation technology. On the one hand, it is very sensitive to imaging conditions such as light and shade, shadow, and illumination of the image, and on the other hand, the subjective parameters involved in the algorithm are too high. Many, it is not easy to make real-time and adaptive adjustments, so the robustness is not strong, and it is generally only used in ...

AI Technical Summary

Problems solved by technology

[0003] The traditional iris extraction technology is generally based on the frontal face image, and is processed by edge extraction or threshold separation technology. On the one hand, it is very sensitive to imaging conditions such as light and shade, shadow, and illumination of the image, and on the other hand, the subjective parameters involved in the algorithm are too high. Many, it is not easy to make real-time and adaptive adjustments, so the robustness is not strong, and it is generally only used in specific environments with good imaging conditions (such as eye movement analyzers in hospitals, helmet-mounted iris detection devices, etc.), but in general It is not suitable for public life application scenarios (such as surveillance cameras, mobile phone cameras, etc.), and there will be a lot of misjudgments and missed detections.

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