Intelligent eyelid detection method and system

Abstract

The invention belongs to the technical field of eyelid image processing, and discloses an intelligent eyelid detection system and method.The method comprises the steps that firstly, an eyelid positioning algorithm is used for positioning the position of an eyelid, an eyelid feature point detection method is used for positioning the position of a canthus point, and the position of the eyelid is calculated through the canthus point; the method comprises the following steps: establishing an iris template, then detecting the position of an eye region by using the iris template, positioning the position of an iris center through an iris center fine positioning algorithm, and finally, taking the detected canthus point and the iris center as eye motion vectors; a neural network model is used, the eye motion vector is used as input of a neural network, and a fixation point mapping relation is established. According to the invention, a good effect is achieved, more stable eye movement characteristics under head movement are selected, a single-camera light-source-free system architecture is adopted, and a 2D sight tracking method based on an artificial neural network is used, so that better precision is achieved; the hardware requirement of the system is reduced, the hardware cost is reduced, and the availability of the system is enhanced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an intelligent eyelid detection method and system. Background technique [0002] The human eye structure is composed of sclera, iris, pupil, lens, retina and other parts. The iris is a ring-shaped part located between the black pupil and the white sclera. It is mainly composed of connective tissue, including many filaments, crowns, stripes, hidden Details of nests, etc. Moreover, after the iris is formed in the fetal development stage, it remains unchanged throughout the life course. These characteristics determine the uniqueness of iris characteristics and also determine the uniqueness of identification. [0003] At present, the new crown epidemic is raging around the world, and the unique characteristic of iris makes iris recognition technology widely used. Iris recognition is the most accurate and efficient biometric technology, and it is also considered...

AI Technical Summary

Problems solved by technology

[0011] However, the above algorithm takes a long time to detect. For example, Adam's algorithm needs 300 milliseconds to detect an eye diagram, which has low detection efficiency and poor robustness.

[0012] The traditional iris positioning method is prone to misjudgment, which has a great impact on the positioning of the iris center and takes a long time

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