Pupil detection method based on edge filtering, ellipse evaluation and pupil verification

Abstract

The invention discloses a pupil detection method based on edge filtering, ellipse evaluation and pupil verification, and the method mainly comprises the steps: firstly, carrying out the normalizationof an input gray level image, carrying out the Canny edge filtering of an eye image, and removing the noise which may damage the peripheral edge of a pupil; based on the physiological characteristicsof the eye image, through the linearity, internal intensity values and elliptical features, carrying out ellipse fitting and selecting an optimal ellipse and a pupil verification check to collect andevaluate the edge of the connection; and if the effective ellipse describing the pupil is found, returning the result, otherwise, performing the next calculation, reducing the image, and after applying the surface difference filter and the mean value filter to the re-scaled image, selecting the optimal position through the product result of the two filters and selecting the maximum value; and scaling the pixel position in the image results in a distance error of the pupil center in the original image. Thus, it must be necessary to optimize the pupil position on the original image based on theanalysis of the surrounding pixels of the selected pupil position.

Description

Technical field [0001] The present invention relates to a method for pupil positioning in biological recognition, in particular to a pupil positioning method for edge filtering, ellipse evaluation and pupil verification based on eye grayscale images. Background technique [0002] Now, eye pupil positioning is used in many biometrics, machine vision, and medical fields. At present, eye pupil positioning is based on automatically detecting the center of the pupil in the eye image to map the gaze point of the scene. Although positioning can be successfully completed under laboratory conditions, many studies have reported difficulties in locating pupils in natural environments, such as fatigue driving detection, iris recognition, automatic shopping, medical treatment, etc. The main noise source in these experimental tasks is the unstable pupil signal, which is mainly related to the algorithm in pupil image-based detection. Now I have summarized the various difficulties in locating t...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiencies in the prior art, the present invention provides a pupil positioning method based on edge filtering, ellipse evaluation and pupil verification, which can solve the current iris recognition, fatigue In terms of detection, the detection time consumption and accuracy of pupil positioning are generally not high in efficiency.

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