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Iris center positioning method based on cascade regression forest and image gray features

A cascading regression, image grayscale technology, applied in character and pattern recognition, instruments, acquisition/recognition eyes, etc., to achieve the effect of low hardware requirements, high robustness, and low computational cost

Inactive Publication Date: 2019-07-09
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of real-time, accuracy and robustness in the prior art, the present invention provides an iris center positioning method based on cascaded regression forest and image grayscale features, which uses cascaded regression forest , gray-scale weighted average and weighted-snakuscule energy value iteration, fully combining the advantages of appearance-based methods and machine learning-based multi-stage schemes, still maintaining high accuracy and robustness under challenging low-resolution images sex

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  • Iris center positioning method based on cascade regression forest and image gray features
  • Iris center positioning method based on cascade regression forest and image gray features
  • Iris center positioning method based on cascade regression forest and image gray features

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Embodiment Construction

[0027] Several exemplary embodiments will be described in detail below with reference to the accompanying drawings to illustrate the principle and spirit of the present invention. However, the described embodiments do not represent all implementations consistent with this application. The purpose of describing these implementation examples is to enable those skilled in the art to better understand the present invention, but not to limit the scope of the present invention in any way.

[0028] like figure 1 Shown, the iris center location based on cascade regression forest and image grayscale feature of the present invention comprises the following steps:

[0029] S101. Acquire the facial image of the target to be detected; the image refers to an image or video in a suitable format collected by the sensor, and the face area obtained after the face detection process. Stage 201 shows an example of face detection, the face Regions are marked with boxes. The scope of the present ...

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Abstract

The invention discloses an iris center positioning method based on a cascade regression forest and image gray features, and relates to the technical field of iris detection. The invention is used forsolving the problems of real-time performance, accuracy, robustness and the like in the prior art, and provides an iris center positioning method based on a cascade regression forest and image gray feature. The method utilizes cascaded regression forests, grayscale weighted averages, and weighted-snakuscule energy value iterations and fully combines advantages of a method based on expressions anda multi-stage scheme based on machine learning. High accuracy and robustness are still kept under challenging low-resolution images.

Description

technical field [0001] The invention relates to the technical field of iris detection, in particular to an iris center positioning method based on cascade regression forest and image grayscale features. Background technique [0002] Eye center positioning generally refers to iris center positioning or pupil center positioning. This technology is widely used in academic research fields such as psychology, medicine, iris recognition and human-computer interaction, and commercial fields such as advertising and web page optimization. At present, the mainstream eye center localization methods based on image / video processing can be divided into three categories: shape-based methods, appearance-based methods and hybrid localization methods. [0003] Shape-based positioning methods are usually based on iris (or pupil) edge extraction and circle (or ellipse) fitting, that is, using the feature that the contour of the iris / pupil is circular or elliptical. However, when image noise or...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/193G06V40/197
Inventor 王睿缜沈海斌
Owner ZHEJIANG UNIV
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