a based on l 2,1 Age Estimation Methods for Partial Label Learning

A marking and labeling technology, applied in the field of computer vision, can solve the problem of complex infinite norm solution without considering local characteristics, and achieve the effect of simple solution

Active Publication Date: 2021-06-01
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, this method does not take into account the local characteristics of the sample, and the solution of the infinite norm is too complicated

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  • a based on l <sub>2,1</sub> Age Estimation Methods for Partial Label Learning
  • a based on l <sub>2,1</sub> Age Estimation Methods for Partial Label Learning
  • a based on l <sub>2,1</sub> Age Estimation Methods for Partial Label Learning

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[0013] The present invention is further illustrated in conjunction with the accompanying drawings. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. After reading the present invention, those skilled in the art will modify various equivalent forms of the present invention All fall within the scope defined by the appended claims of this application.

[0014] Such as figure 1 As shown, the implementation of the present invention mainly includes four steps: (1) obtain the feature matrix and the label matrix of the face data set, and map to the kernel space for processing; (2) construct a method based on L 2,1 The objective function of the partial label learning method with norm and manifold assumptions; (3) using the alternate iterative method to solve the optimization problem, and obtain the sample label distribution matrix Z (t+1) and discriminant coefficient A (t)...

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Abstract

The invention discloses a method based on L 2,1 Age Estimation Methods for Partially Labeled Learning. First obtain the feature matrix and label matrix of the face dataset, and then construct the objective function of the method. In order to make the label distribution of the samples as sparse as possible and the solution easier, L is embedded in the objective function 2,1 Norm; in order to make the label distribution of adjacent samples as similar as possible, the idea of ​​manifold assumption is adopted, and the graph regularization term is embedded in the objective function. Then use the alternate iterative method to solve the optimization problem, and obtain the discriminant coefficient A (t) . Finally, using the discriminant coefficient A (t) Estimate the label distribution of a given face test sample, and determine its age according to the principle of maximum probability. On the one hand, the present invention makes full use of the potential useful information of the feature space, makes the label distribution of adjacent samples as close as possible, and effectively improves the accuracy and robustness of the method; on the other hand, it can disambiguate the candidate label set, Accurately estimate the label of the sample.

Description

technical field [0001] The present invention relates to a kind of based on L 2,1 An age estimation method for partial label learning, belonging to the field of computer vision. Background technique [0002] In recent years, with the development of computer vision, face recognition technology has become a very popular research topic in the field of pattern recognition. The human face is one of the very important biological characteristics of human beings. The human face contains a lot of important information, such as identity, gender, age, expression, race and so on. Among all the information, the age information displayed by the face is an important basis for human identification, and the age estimation of the face image has a very good application prospect in the fields of human-computer interaction, computer vision, and smart business. Therefore, face age estimation has attracted more and more scholars' attention. [0003] The existing face age estimation algorithms ma...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/178G06V40/16G06V40/168
Inventor 夏思雨甘海涛郭丽厉振华
Owner HANGZHOU DIANZI UNIV
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