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Image main information extraction method based on modular PCA and human face recognition method

An extraction method and technology of main information, applied in the field of face recognition, can solve problems such as slow processing speed and inability to meet processing speed, and achieve the effects of good robustness, reduced complexity, and reduced random errors

Active Publication Date: 2017-06-23
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

However, with the improvement of photography and image technology, when it is applied to real life, for face pictures with higher pixels, due to the method of overlapping sampling and segmentation, when the PCA or 2D-PCA face recognition method is used, the processing is difficult. The speed is slow and cannot meet the needs of processing speed

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  • Image main information extraction method based on modular PCA and human face recognition method
  • Image main information extraction method based on modular PCA and human face recognition method
  • Image main information extraction method based on modular PCA and human face recognition method

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Embodiment Construction

[0054] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0055] Due to the modular PCA, especially the block processing in the case of overlapping blocks increases the computational complexity, resulting in a slowdown in real-time face matching and face recognition. This application improves on this problem. After block , using a statistically randomized algorithm for module matching, which improves the recognition speed while retaining the advantages of modular PCA.

[0056] This application is an improvement on the current modular PCA, so first briefly introduce the modular PCA algorithm:

[0057] When performing face recognition, it is ...

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Abstract

The invention relates to an image main information extraction method based on modular PCA and a human face recognition method. The method comprises the steps that an image is divided into a number of sub-images in an overlapping block dividing manner; normalized sub-images are acquired according to the desired matrix processing sub-image of all sub-images; designated number of rows and columns of normalized sub-images are randomly selected according to the row-important sample probability and the column-important sample probability to form the sub-image main information matrix; and finally the main information matrixes of an image to be recognized and the image are compared to recognize the image to be recognized. Compared with the prior art, the method provided by the invention has the advantages that after block dividing, a statistics randomization algorithm is used to carry out module matching; and on the premise that the modular PCA advantage is retained, the recognition speed is improved.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a modular PCA-based image main information extraction method and a face recognition method. Background technique [0002] Face recognition is an important research field in biometric technology, and has broad application prospects. Both in theory and in practice, it has great significance. It covers digital image processing, neural network, psychology, physiology, pattern recognition, computer vision and artificial intelligence. development has important theoretical significance. It also has great application value in various fields such as public security, finance, network security, property management and attendance. For example, face recognition technology can quickly calculate the similarity between the face data collected in real time and the face data of known persons in the face image database, and return the identification result and corresponding credibility. For example, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62
CPCG06V40/172G06V10/267G06F18/22G06F18/2135
Inventor 赵生捷陈栋杨恺
Owner TONGJI UNIV
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