Three-dimensional extraction method of human face wrinkles shown in point cloud data and device thereof

Abstract

A three-dimensional extraction method of human face wrinkles shown in point cloud data includes the steps of simulating a generation process of human face wrinkles, setting a wrinkled area as the superposition of a smooth human face curved surface with sunken wrinkle textures, setting human face wrinkles shown in point cloud data as an object of study, remaking the smooth human face area with a computer, fitting the smooth human face area through a triplet spline curved surface fitting method, constraining triplet spline curved surface fitting, setting a smooth human face fitting process as a solving process of a regular problem, performing iterative solution through a Laplace regularization method to obtain a preliminary result epsilon 0 of human face wrinkle extraction, projecting the human face wrinkle extraction to an X-Y plane, performing binarization and 8 neighborhood communication region calculation on a projection image, setting a communication region 30% smaller than the largest communication region as noise, and removing points corresponding to the noise from the epsilon 0 to obtain a final three-dimensional extraction result of the human face wrinkles.

Description

technical field [0001] The invention belongs to the field of biomedical engineering, and in particular relates to a three-dimensional extraction method and equipment for human face wrinkles represented by point cloud data. Background technique [0002] Wrinkles are an important feature that gradually appear on the human face as we age. Wrinkle characterization and wrinkle evaluation have important application value in many fields, the basis of which lies in the extraction of wrinkle data. [0003] Early wrinkle extraction methods used silicone molds to obtain skin surface replicas (skin surface replicas, SSRs). Taking SSRs as the research object, several mechanical parameters were used to evaluate wrinkles. Due to extrusion deformation and other reasons, there must be differences between SSR and real wrinkles, so the SSR method can only be used as a preliminary evaluation of wrinkles, and the error is large. The current commonly used wrinkle evaluation method is to use the...

AI Technical Summary

Problems solved by technology

Due to extrusion deformation and other reasons, there must be differences between SSR and real wrinkles, so the SSR method can only be used as a preliminary evaluation of wrinkles, and the error is large

The current commonly used wrinkle evaluation method is to use the wrinkle evaluation scale to realize the wrinkle data. The wrinkle data is mainly two-dimensional image data. The "extraction" of wrinkle data is completed through the evaluator's subjective visual perception. This method cannot avoid subjective factors. impact, and wrinkle data cannot complete storage and other functions

In the 1980s, Chernoff and others used the laser ranging method to conduct research on wrinkle detection, but the entire detection process needs to be scanned point by point, which takes a long time, and it is difficult to ensure the absolute position of the human body during the entire test process.

In recent years, with the rapid development of computer vision and image processing technology, many researchers have adopted wrinkle extraction methods based on two-dimensional image grayscale and texture processing, but the extracted wrinkle features are limited by factors such as field of view, illumination, and shooting angle. , and because the wrinkle itself is a three-dimensional shape, the accuracy and repeatability of this method need to be improved

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