Method for searching new position of feature point using support vector processor multiclass classifier

A support vector machine and feature point technology, applied in the field of image processing, can solve the problems of inaccuracy of the new position of the search feature point, unbalanced number of positive and negative samples, etc., and achieve the effects of improved accuracy, improved accuracy, and high precision.

Inactive Publication Date: 2006-05-24
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0004] Aiming at the defect of inaccuracy when searching for the new position of the feature point in the ASM method, the present invention proposes a method for searching the new position of the feature point with a support vector machine multiclass classifier, so that it converts the problem of searching for the new position of the feature point Solved for the classification problem, thus solving the problem of the background technology
In essence, this belongs to two types of classification problems, but for the problem of the imbalance of the

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  • Method for searching new position of feature point using support vector processor multiclass classifier
  • Method for searching new position of feature point using support vector processor multiclass classifier
  • Method for searching new position of feature point using support vector processor multiclass classifier

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Embodiment Construction

[0022] The technical solution of the present invention will be further described in detail below in conjunction with a specific embodiment.

[0023] The images used in the embodiment are from the captured face image library. The whole implementation process is as follows:

[0024] 1. Select 600 face images marked with feature points from the face database to build an ASM model. A face image with marked feature points, such as figure 1 shown. That is, at first, 60 feature points are selected on each training sample image of the training set, and the shape formed by these 60 feature points can be composed of a vector x(i)=[x 1 , x 2 ,...,x 60 ,y 1 ,y 2 ,...,y 60 ] to indicate that the feature points with the same number represent the same feature in different images, 600 training sample images have 400 shape vectors, and then perform calibration operations on these 400 vectors to make the shape represented by these shape vectors closest in size, orientation and position...

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Abstract

The method includes steps: (1) building ASM model, and initializing ASM model so as to obtain initial position of model; (2) generating multiple classes of training samples corresponding to each feature point on human face in order to train classifier in multiple classes of vector machine; (3) using training sample corresponding to each feature point trains the train classifier in multiple classes of vector machine; (4) using initial position of model as initial position of ASM searching, and using the said classifier to locate each feature point. The method is related to detecting eye, training classifier, converting classifier in two classes to classifier in multiple classes, and ASM location for feature point. Further, the invention is applicable to recognition of human face, recognition of sexuality etc. possessing high precision.

Description

technical field [0001] The invention relates to a method in the technical field of image processing, in particular to a method for searching new positions of feature points by using a support vector machine multiclass classifier. Background technique [0002] Face feature point location is the core technology in face recognition, expression recognition, gender recognition, age estimation, and pose estimation. It is an indispensable intermediate link in face detection and face recognition. The accuracy of feature point location It directly determines the recognition accuracy. Therefore, accurately locating a large number of facial feature points can greatly improve the accuracy of recognition. The existing facial feature location methods are mainly divided into two categories: the first category is the local feature point location method, and the second category is the global feature point location method. Although the local face feature point positioning method is fast, it...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 杜春华杨杰
Owner SHANGHAI JIAO TONG UNIV
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