Unsupervised face recognition method based on fast density clustering algorithm

A technology of density clustering algorithm and recognition method, which is applied in the field of unsupervised face recognition based on fast density clustering algorithm, can solve the problem that the face recognition algorithm cannot obtain the expected recognition effect, and achieve good performance and good clustering Performance and recognition performance, the effect of ensuring accuracy

Active Publication Date: 2016-07-06
杭州阳明信息科技有限公司
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Problems solved by technology

However, when the classification of training samples is not clear, the exist

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  • Unsupervised face recognition method based on fast density clustering algorithm
  • Unsupervised face recognition method based on fast density clustering algorithm
  • Unsupervised face recognition method based on fast density clustering algorithm

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings.

[0044] refer to Figure 1 ~ Figure 3 , a kind of unsupervised face recognition method based on fast density clustering algorithm, described recognition method comprises the steps:

[0045] 1) Aiming at the pixel information of the face image, the similarity between images is obtained by using the structural similarity calculation method. The calculation method of structural similarity is defined as follows:

[0046] In the spatial domain, two image blocks x={x i i=1,...,M} and y={y i The structural similarity between i=1,...,M} is:

[0047] S ( x , y ) = ( 2 μ x μ y + C 1 ...

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Abstract

The invention discloses an unsupervised face recognition method based on a fast density clustering algorithm. The unsupervised face recognition method comprises the steps: firstly extracting pixel matrixes of face images; by utilizing structure similarity, computing similarity of the images; by utilizing the Gaussian function, computing the density of image objects; by utilizing the computed density of the image objects, computing the local density of one image object, and the minimum distance between the image object and another image object with the larger local density; with the combination of density-distance distribution of the image objects, fitting a density and distance function relationship by regression analysis, and automatically determining a cluster center by residual analysis; training and recognizing clustering results by utilizing a classifier. The unsupervised face recognition method based on the fast density clustering algorithm, which is provided by the invention, does not need to predict any classification information of the face images and has stronger recognition capability.

Description

technical field [0001] The invention belongs to an unsupervised face recognition method, and aims at the existing problems of the face recognition method, and proposes an unsupervised face recognition method based on a fast density clustering algorithm. Background technique [0002] With the rapid development of new technologies such as information technology, artificial intelligence, pattern recognition, and computer vision, face recognition technology has various potential applications in security systems such as public security and transportation, and has received extensive attention. Face recognition is mainly to automatically extract face features from face images, and then perform identity verification based on these features. Face recognition methods can be divided into the following categories according to different algorithms: face recognition based on geometric features, face recognition based on subspace analysis, face recognition based on elastic graph matching, ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06F18/24147G06F18/23G06F18/2411
Inventor 陈晋音何辉豪陈军敢杨东勇
Owner 杭州阳明信息科技有限公司
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