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Mutual conversion method of visible light and near-infrared human face images

A mutual conversion, infrared human technology, applied in the field of face recognition, can solve the problems of blurred results, unable to truly reflect nonlinear relationships, lack of detailed information, etc.

Inactive Publication Date: 2015-06-10
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method assumes that the mapping between near-infrared face images and visible light face photos is a linear relationship, which cannot truly reflect the nonlinear relationship between the two, resulting in fuzzy results and missing details;

Method used

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  • Mutual conversion method of visible light and near-infrared human face images
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  • Mutual conversion method of visible light and near-infrared human face images

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Embodiment Construction

[0059] The implementation of the present invention is described below through specific examples and in conjunction with the accompanying drawings, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific examples, and various modifications and changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.

[0060] figure 1 It is a flow chart of the steps of a method for mutual conversion between visible light and near-infrared face images of the present invention. As shown in the figure, a method for mutual conversion of visible light and near-infrared face images of the present invention includes the following steps:

[0061] Step 101, using a method based on sparse learning to convert the n...

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Abstract

The invention discloses a mutual conversion method of visible light and near-infrared human face images. The method comprises the steps of converting a near-infrared human face image or a visible light human face photo into an initial visible light human face photo or an initial near-infrared human face image by use of a method based on sparse learning, and converting the initial visible light human face photo or the initial near-infrared human face image into a high-definition detail photo of the visible light human face photo or the near-infrared human face image by use of a method based on multi-characteristic selection. According to the mutual conversion method, heterogeneous human face images are fitted in a stratified manner by use of a method based on sparse regularization and the visible light human face photo is generated from the near-infrared human face image, and therefore, the detail information of the synthetic photo is increased and the problem of heterogeneous human face recognition is solved.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a method for mutual conversion of visible light and near-infrared face images based on sparse learning. Background technique [0002] Face recognition technology uses computers to obtain face images, analyze and process them, then extract features that can effectively represent face images in a specific way, and finally identify the face images through its learning method. Face recognition is widely used in security verification systems, verification of driver's licenses and passports, and criminal identification. With the development of information and network technology in recent years, face recognition has become one of the most concerned issues in the field of pattern recognition. [0003] Illumination is a key factor affecting accuracy in face recognition systems. Near Infrared (NIR) imaging is robust to changes in ambient light within a certain limit, and can meet the requ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 倪辉苏剑波
Owner SHANGHAI JIAO TONG UNIV
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