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Two-dimensional linear discrimination human face analysis identificating method based on interblock correlation

A face recognition and correlation technology, which is applied in the field of pattern recognition, can solve the problems of unfavorable face recognition, little help in determining the main component, and splitting the pixel correlation in local areas of the face.

Inactive Publication Date: 2008-08-13
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This has two disadvantages: first, 2DFDA completely discards the correlation information of pixels between rows; second, the pixels that are far apart in the same row have weak correlation, which is not helpful for determining the pivot. Large, useless computing resources
In face recognition, the recognition features of the face are mainly manifested in local areas, such as the main organs of the face, eyes, nose, mouth, etc., all belong to the local features of the face, but the area used by the 2DFDA method is within the same line of the image. pixels, which split the correlation of pixels in the local area of ​​the face, which is not conducive to the correct recognition of the face

Method used

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  • Two-dimensional linear discrimination human face analysis identificating method based on interblock correlation
  • Two-dimensional linear discrimination human face analysis identificating method based on interblock correlation
  • Two-dimensional linear discrimination human face analysis identificating method based on interblock correlation

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Experimental program
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Embodiment Construction

[0065] The following takes the FERET face database as an example to illustrate the implementation process of this method.

[0066] Choose 60 people in the FERET library, each with 6 images, a total of 360 images form the training and test face library, including faces under different expressions, light, occlusion, etc. The image size is 112×92.

[0067] Randomly select 3 images / person from the library for training, and the remaining 3 images / person for testing. The random sampling experiment was repeated 30 times, and the average recognition rate was taken as the final recognition rate. The specific implementation process is as follows:

[0068] 1. Database construction stage

[0069] (1) Image standardization

[0070] Preprocess the image, including light normalization and size normalization. After preprocessing, the gray levels of all images are unified to the standard level, and the gray levels are relatively clear (because of the histogram equalization). The image size after p...

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Abstract

This invention relates to an 2DFDA man-face identification method based on block relativity, which divides a man-face image into non-overlapped small blocks according to the regianl characters of a man-face image, then connects the elements in each small block in lines to generate related line vectors and arranges the vectors in sequence to a 2-D image matrix to be taken as the input image finally for 2DFDA identification, which fully utilizes the relativity information between line and row pixels in local regions and remains the local character information to get rather high identification rate of man faces.

Description

Technical field [0001] The present invention belongs to the technical field of pattern recognition, and specifically relates to a face recognition method based on two-dimensional linear discriminant analysis based on intra-block correlation. technical background [0002] Face recognition belongs to the category of pattern recognition. As one of the most successful applications in the field of image analysis and understanding, face recognition has received extensive attention in both commercial applications and research fields. The existing face recognition methods include face recognition methods based on statistical analysis and face recognition methods based on template matching. [0003] A specific face image can be represented by an n×n matrix A, or it can be represented by an n×n-dimensional vector I. Due to the high dimensionality of the data space, the comparison during recognition is quite difficult, and dimensionality reduction methods are usually used to compress the da...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
Inventor 马争鸣胡海峰李莹张成言
Owner SUN YAT SEN UNIV