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Face recognition method

A face recognition and to-be-recognized technology, applied in the field of face recognition, can solve the problem of not completely solving the problem of singular matrix inversion and high computational complexity, and achieve the effects of improving computational speed, high robustness, and wide application range.

Inactive Publication Date: 2011-11-23
WUXI ZGMICRO ELECTRONICS CO LTD
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Problems solved by technology

The main disadvantage of linear discriminant analysis is that it is necessary to stretch the face image into a one-dimensional vector and then perform the operation. The operation complexity is relatively high, and it is necessary to face the problem of singular matrix inversion operation.
In response to this situation, two-dimensional linear discriminant analysis (2DLDA) has recently appeared. This method avoids the problem that the matrix must be stretched into a one-dimensional vector before processing in LDA, improves the operation speed, and also solves the singularity to a certain extent. Matrix inversion problem, but 2DLDA does not completely solve the singular matrix inversion problem

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Embodiment Construction

[0020] The specific implementation manners of the present invention will be described below in conjunction with the accompanying drawings.

[0021] The purpose of the present invention is to further improve the two-dimensional linear discriminant analysis, and thoroughly solve the singular matrix inversion problem in the two-dimensional linear discriminant analysis.

[0022] figure 1 It is a schematic flowchart of the face recognition method 100 in an embodiment of the present invention. see figure 1 As shown, the face recognition method 100 includes the following steps.

[0023] Step 110, training phase: establish a face training database including multiple face training images, construct a projection matrix based on all face training images in the face training database, and project all face training images in the face training database to The feature matrix of each training image is obtained on the projection matrix.

[0024] figure 2 A schematic flow chart of the tra...

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Abstract

The invention discloses a face recognition method comprising the following steps of: establishing a face training database including a plurality of face training images, structuring a projection matrix based on all the face training images of the face training database, and projecting all the face training images in the face training database onto the projection matrix to obtain the characteristic matrixes of all the training images, respectively; projecting an image to be recognized to the projection matrix to obtain the characteristic matrix of the image to be recognized, calculating a distance between the characteristic matrix of the image to be recognized and the characteristic matrix of each training image, and selecting the type of the characteristic matrix of the training image having the shortest distance away from the characteristic matrix of the image to be recognized as the type of the image to be recognized. Therefore, no matrix inversion operation is needed in the method, and the restraint of the inversion operation of a singular matrix is avoided; and the method is higher in robustness and wider in application range, compared with Two-Dimensional Linear Discrimination Analysis (2DLDA).

Description

【Technical field】 [0001] The invention relates to the field of image processing, in particular to a face recognition method. 【Background technique】 [0002] Face recognition is an important research field of computer vision. It has broad market prospects and application value in many aspects such as human-computer interaction, access control, and intelligent monitoring. [0003] The current existing face recognition methods mainly use linear discriminant analysis (LDA) to extract the features of training faces, and then design classifiers to distinguish different types of face images. The main disadvantage of linear discriminant analysis is that the face image must be stretched into a one-dimensional vector and then operated, the computational complexity is relatively high, and it is necessary to face the problem of singular matrix inverse operation. In response to this situation, two-dimensional linear discriminant analysis (2DLDA) has recently appeared. This method avoids...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 王磊
Owner WUXI ZGMICRO ELECTRONICS CO LTD
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