Three-dimensional human head point cloud feature extraction method and device thereof

Abstract

The invention discloses a three-dimensional human head point cloud feature extraction method and a device thereof. The method comprises steps that 1, point cloud data pre-processing, sampling, outlier removal and noise removal are included; 2, training stage, a normal and an FPFH (fast point feature histogram) value of each point are calculated, and a K-dimensional tree is established; 3, FPFH values of three typical feature portions are segmented and extracted from a given model; 4, searching is carried on the K-dimensional tree established in the step 2, and a candidate set is searched; and 5, a principal curvature of each point of the candidate set and a shape response factor are calculated, a feature point is finally determined according to profiles of the three typical features and the position of a nose tip. Through the method, sensitivity to illumination and angles during two-dimensional feature extraction in the prior art is overcome, training of the large-scale data is not required, and rapid and accurate extraction of the three typical human head features is realized, and typical features of the nose tip, a left auditory meatus and a right auditory meatus can be extracted on the basis of the three-dimensional human head point cloud data.

Description

technical field [0001] The invention relates to the fields of image processing, computer graphics, computer vision, etc., and in particular to a method for extracting features of a three-dimensional human head point cloud with high measurement accuracy and high measurement speed for feature analysis and extraction based on fast point feature histogram values. Background technique [0002] In recent years, the field of computer vision has developed rapidly and has been widely used in industrial robots, unmanned vehicles, medical image analysis and topology modeling, virtual reality and augmented reality, artificial intelligence and other fields. In many of the above applications, facial feature extraction is a crucial step, such as face recognition, pose estimation, modeling, tracking, etc. [0003] The face recognition algorithms based on two-dimensional images are all aimed at frontal portraits. When the pitch angle, illumination, posture, makeup, age, etc. of the two-dimen...

AI Technical Summary

Problems solved by technology

In the human head tissue, the nose and ears are least affected by changes in expression, makeup, age, etc., and the three-dimensional geometric information is the most prominent and abundant. However, there is no mature three-dimensional measurement that can quickly and accurately extract the human nose and ears. technology

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