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Automatic positioning method for characteristic point of human faces

A facial feature, automatic positioning technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problem of easy to fall into local minimum, can not completely solve the problem of local minimum.

Inactive Publication Date: 2006-08-02
FUDAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The improved optimization algorithm basically solves the speed problem of the AAM algorithm, but still cannot completely solve the local minimum problem, and, because the optimization is based on the average texture error, and the texture of the cheek part of the face is relatively flat, the traditional Both the AAM algorithm and the real-time AAM algorithm are easy to fall into the local minimum when extracting the feature points on the chin

Method used

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  • Automatic positioning method for characteristic point of human faces
  • Automatic positioning method for characteristic point of human faces
  • Automatic positioning method for characteristic point of human faces

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Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0096] 1. Create a shape and texture model:

[0097] 1. Shape model:

[0098] Arrange v feature point coordinates on each picture as a shape vector, S=(x 1 ,...,x v ,y 1 ,...,y v )', S t ∈ R 2v . Then use the following method to normalize the shape vectors of N images:

[0099] (a) Remove the mean of all shape vectors and transfer to the centroid coordinate system.

[0100] (b) Choose a sample as the initial mean shape, and calibrate the scale such that |S|=1.

[0101] (c) Denote the initial estimated mean shape as And defined as a reference frame.

[0102] (d) Calibrate all training sample shapes to the current mean shape by affine transformation.

[0103] (e) Recalculate the mean shape for all samples after calibration.

[0104] (f) Calibrate the current mean shape to , and make |S|=1.

[0105] (g) If the change in average shape is still larger than the given threshold, go back to (d).

[0106] The statistical shape model is established by the PCA method of ...

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Abstract

Present invention is a fast human face characteristic point positioning method. Said algorithm can fast locate out a lot of facial prominent feature point position to an arbitrary inputted human face front or side (deflection angle deflection angle within 45 degree) digital image. Said algorithmic can expand to use on other objective characteristic point positioning. Said method comprehensive utilizes human facial figure and texture information, establishing changeable shape and texture pattern, initializing model parameter, adopting real time AAM and genetic algorithm genetic algorithm to optimize shape factor, finally fine adjusting part characteristic point through edge detection and complexion interval detective method, to obtain precise characteristic point positioning.

Description

technical field [0001] The invention belongs to the technical field of machine vision and image processing, and in particular relates to a method for automatically locating feature points in frontal and side face images. technical background [0002] Accurate and fast face feature point location has a very wide range of applications in face recognition and 3D face image reconstruction. Face feature point technology is generally combined with face detection technology to narrow the area for feature point search and become a practical system. [0003] In terms of face detection, the more famous face detection algorithm based on Adaboost proposed by Paul Viola and Michael Jones in 2001 [1], this method is an improvement of statistical learning, it achieves by combining a large number of simple classifiers Face Detection. Since each of the simple classifiers uses features with very fast calculation speed, it fundamentally solves the speed problem of detection. Accurate and fa...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
Inventor 刘成明张立明
Owner FUDAN UNIV