Eyebrow image segmentation method based on semi-supervision learning and Hash index

Abstract

The invention discloses an eyebrow image segmentation technology based on semi-supervised learning and Hash index, which comprises the following steps in sequence: accepting an original eyebrow image of a user, separating the eyebrow image into small pixel blocks with equal dimension and selecting certain pixel blocks respectively outside and inside the eyebrow area by a computer to endow such blocks with different symbols; expressing each pixel block as a vector and calculating the similarity between such pixel blocks through the Hash method of partial sensitiveness so as to obtain a normalized resemblance distance matrix; and using a technology based on semi-supervised learning of the image to mark unmarked pixel blocks from which pixel blocks marked as eyebrow are picked up to finish the extraction of eyebrow. The technology greatly improves the image segmentation speed by the technology based on semi-supervised learning due to the utilization of the Hash method of partial sensitiveness to solve the resemblance distance matrix in the semi-supervised learning technology of the image.

Description

technical field [0001] The invention relates to a method for extracting pure eyebrow images from original eyebrow images based on the combination of semi-supervised learning technology and local sensitive hash index method, which belongs to the field of electronic information technology. Background technique [0002] In modern society, with the rapid development of computer network technology and the rapid rise of e-commerce worldwide, information security has shown unprecedented importance, and biometric identification, as an important aspect of information security, has become more and more popular. Pay attention to. At present, the biometric identification technologies that people research and use mainly include: face recognition, iris recognition, fingerprint recognition, hand shape recognition, palmprint recognition, ear recognition, signature recognition, voice recognition, gait recognition, and so on. As an important feature of human face, eyebrows have universality,...

AI Technical Summary

Problems solved by technology

In general, the segmentation of eyebrow images is more difficult than ordinary image segmentation due to the influence of hair, lighting, pose and expression

The automatic segmentation method is almost impossible to correctly extract the eyebrow image. Other eyebrow image segmentation methods such as principal component analysis and template matching can basically locate the eyebrow, but they cannot accurately calibrate the boundary of the eyebrow.

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