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Multi-task learning based method for recognizing race and gender through human face image

A multi-task learning, face image technology, applied in the field of face image race and gender recognition based on multi-task learning, can solve the problem of insensitive kernel selection

Inactive Publication Date: 2016-08-24
BEIJING UNION UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0008] 3. Relatively insensitive to kernel selection

Method used

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  • Multi-task learning based method for recognizing race and gender through human face image
  • Multi-task learning based method for recognizing race and gender through human face image
  • Multi-task learning based method for recognizing race and gender through human face image

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Embodiment Construction

[0062] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings.

[0063] When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated.

[0064] The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of approaches consistent with aspects of the invention as recited in the appended claims.

[0065] The terminology used in the present invention is for the purpose of describing particular embodiments only, and is not intended to limit the present invention.

[0066] As used herein and in the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise.

[0067] The term "and", "or" used ...

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Abstract

The invention provides a multi-task learning based method for recognizing race and gender through a human face image, relates to the technical fields such as the digital image processing field, the mode recognizing field, the computer vision field and the physiology field, and aims to solve the problems of recognizing of the race and the gender from a static human face or a video human face under a plurality of occasions. The multi-task learning method is a learning method for improving the learning performance through related task learning and has the advantages of recognizing the learning difference between the tasks, sharing related features of the tasks, improving the learning performance through the relevance, and reducing the high-dimension small sample over-learning problem. The multi-task learning method can be applied to the human face based race and gender recognizing; different semantics are treated as different tasks, and on that basis, the semantics-based multi-task feature selection is proposed. With the adoption of the method for recognizing the race and the gender, the generalization capacity of a learning system and the recognizing effect can be obviously improved.

Description

technical field [0001] The invention relates to a face image race and gender recognition method based on multi-task learning, and the method relates to technical fields such as digital image processing, pattern recognition, computer vision, and physiology. Background technique [0002] Face images contain a wealth of information. From the perspective of pattern recognition, it can be used for ethnic identification, gender identification, identity identification, etc. [0003] Principal component analysis (Principle Component Analysis, PCA), its basic idea is to extract the main characteristics of the sample through the K-L transformation, and obtain the expansion base by solving the eigenvector of the covariance matrix of the training sample, and sorting in descending order of the eigenvalues ​​represents the importance of the principal components. degree. Kirby (Turk, M., Pentland, A., Eigenface for Recognition [J]. Journal of cognitive Neuroscience. Vol. 3, No. 1, 1991, p...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06K9/62G06K9/46G06K9/40
CPCG06V10/30G06V10/507G06V10/513G06V30/194G06F18/2411
Inventor 袁家政赵新超
Owner BEIJING UNION UNIVERSITY
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