Face recognition method

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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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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