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A Face Recognition Method

A face recognition and face testing technology, applied in the field of face recognition, can solve problems such as poor face recognition ability, and achieve the effect of distinguishing power, more compactness, and high information entropy

Active Publication Date: 2017-06-23
SHENZHEN INST OF ADVANCED TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] The embodiment of the present invention provides a face recognition method, which aims to solve the problem of relatively poor recognition ability of faces across age stages in the prior art

Method used

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Embodiment 1

[0028] figure 1 The implementation flow of the face recognition method provided by Embodiment 1 of the present invention is shown, and the details are as follows:

[0029] In step S101, the original test face image is preprocessed.

[0030] In this embodiment, for the original test face image, the following preprocessing is done first, and the specific preprocessing steps include:

[0031] Step 1. Properly rotate the original test face image to ensure that the test face image is in a horizontal position. The specific method is to make the line connecting the two eyes parallel to the horizontal line.

[0032] Step 2. Appropriate scaling is performed on the test face image to ensure that the distance between the two eyes in the test face image is a fixed value.

[0033] Step 3. Cut off the non-face parts (such as the background) in the test face image, and only keep the face part. The size of the cut face image is 200*150.

[0034] Step 4. Perform histogram equalization on th...

Embodiment 2

[0062] image 3 The implementation process of the face recognition method provided by Embodiment 2 of the present invention is shown, and the details are as follows:

[0063] In step S301, the original test face image is preprocessed.

[0064] In step S302, the original codeword of the test face image is extracted based on the multi-scale local binary model LBP descriptor by means of raster scanning.

[0065] In step S303, count the frequency distribution of each original codeword, encode the original codeword based on the learned LBP code to obtain a set of new codewords, and calculate the LBP feature of the test face image based on the new codeword generated after encoding .

[0066] In step S304, the LBP features are processed using a cascaded subspace training model to obtain low-dimensional features corresponding to the LBP features.

[0067] In this embodiment, the LBP features obtained in step S303 often have very high dimensions, so it is necessary to train an appro...

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Abstract

The invention relates to the technical field of face recognition, and provides a face recognition method. The method includes the steps of preprocessing an original test face image; by means of a grating scanning mode and on the basis of a multi-scale local binary model LBP descriptor, extracting original code words of the test face image; counting frequency distribution of each original code word, carrying out encoding to obtain a group of new code words on the basis of learnt LBP encoding, and calculating LBP features of the test face image on the basis of the new code words generated after encoding; according to the LBP features of the test face image, recognizing the test face image. By means of the face recognition method, probability distribution of the new code words generated through encoding is more uniform, so that a final encoding space is more compact, higher information entropy is achieved, more original information is kept, and higher identification capacity is achieved.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a face recognition method. Background technique [0002] Automatic face recognition has always been a very important and challenging research topic. The reason for its difficulty mainly comes from the following two aspects: (1), the human face has a strong structural similarity. The face composition of different people is similar (all are composed of eyes, mouth, nose and other parts, and these parts are in relatively fixed positions); , different lighting and other conditions will have a great change. [0003] In face recognition, the face difference between different individuals is usually called the inter-class variation of the face, and the face variation of the same individual under different circumstances is called the intra-class variation of the face. Generally speaking, in face recognition, especially in cross-age face recognition, the intra-class...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
Inventor 李志锋龚迪洪乔宇刘建庄汤晓鸥
Owner SHENZHEN INST OF ADVANCED TECH
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