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L2,1 partial mark learning-based age estimation method

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: 2019-09-20
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

Method used

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Embodiment Construction

[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] like 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 an L2,1 partial mark learning-based age estimation method. Firstly, a feature matrix and a label matrix of a face data set are obtained, and then an objective function of the method is constructed. In order to enable the label distribution of the sample to be as sparse as possible and to be simpler to solve, L2,1 norm is embedded into an objective function; in order to enable label distribution of adjacent samples to be similar as much as possible, a manifold hypothesis thought is adopted, and a graph regularization item is embedded into an objective function. Then, an optimization problem is solved by utilizing an alternating iteration method to obtain a discrimination coefficient A(t); and finally, the label distribution of a given face test sample is estimated by using the discrimination coefficient A(t), and the age of the face test sample is judged according to a probability maximization principle. On one hand, potential useful information of a feature space is fully utilized, so that label distribution of adjacent samples is close as much as possible, and the accuracy and robustness of the method are effectively improved; on the other hand, disambiguation can be carried out on the candidate mark set, and the label of the sample can be accurately estimated.

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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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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