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

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 invention belongs to the technical field of pattern recognition, and in particular relates to a two-dimensional linear discrimination analysis face recognition method 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. 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 can be represented by an n×n-dimensional vector I. Due to the high dimensionality of the data space dimension, it is quite difficult to compare during recognition, and it is usually necessary to use a dimensionality reduction method to compre...

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 the face recognition, the recognition features of the face are mainly manifested in local areas, such as the main organs of the face, such as eyes, nose, and 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. The pixels of the face split the correlation of the pixels in the local area of ​​the face, which is not conducive to the correct recognition of the face.

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