Face recognition method based on extraction of multiple evolution features

A feature extraction and face recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of not taking into account the spatial relationship between two samples, not being able to generalize globally, etc., to achieve easy implementation and improve reliability. , the effect of simple principle

Inactive Publication Date: 2013-11-13
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

However, the common shortcomings of these methods are obvious, just like the problems of PCA and LDA, 2DPCA and 2DLDA cannot take into account the spatial relationship between two samples, and can

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  • Face recognition method based on extraction of multiple evolution features
  • Face recognition method based on extraction of multiple evolution features
  • Face recognition method based on extraction of multiple evolution features

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[0035] The present invention will be further described in detail below with reference to the drawings and specific embodiments of the specification.

[0036] Such as figure 1 As shown, the face recognition method of the present invention based on evolutionary multi-feature extraction includes the following steps:

[0037] (1) Take a part of all data set D as training samples. This part of the training sample is divided into 2 parts: one part is D 1 , Contains M 1 Samples for feature extraction; the other part is D 2 , Contains M 2 A sample is used for weight evolution. The size of the training sample will affect the result.

[0038] (2) In the same sample set D 1 ={x 1 ,x 2 ,...,X M1 } Above, a variety of subspace methods are used to construct features. In order to facilitate the construction of features, it is first necessary to convert the picture into a vector form, and then the process of feature extraction. Various methods such as PCA, LDA, LPP, KPCA, etc. can be used according...

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Abstract

The invention discloses a face recognition method based on extraction of multiple evolution features. The method comprises the steps as follows: (1), classification of initial samples: the initial samples are divided into three parts, including training samples for feature extraction, training samples for weight evolution and test samples respectively; (2), feature extraction of the training samples: the training samples are subjected to feature extraction with a multiple seed space method, such as PCA (principal component analysis), LDA (linear discriminant analysis), LPP (locality preserving projection) or the like; and (3), multiple feature fusion evolution: features obtained with different feature extraction methods are fused according to a form that Phi is equal to the sum of Omega 1 Phi 1, Omega 2 Phi 2, ..., and Omega n Phi n, and the like, wherein Omega is a weight coefficient. An optimal weight coefficient is obtained with a genetic algorithm, so that fused features have better recognition effects than prior features. The face recognition method has the advantages that the principle is simple, the method is unique, the application is easy, and the like.

Description

technical field [0001] The invention mainly relates to the fields of feature extraction and face recognition, in particular to a face recognition method based on evolutionary multi-feature extraction suitable for a face recognition system. Background technique [0002] Currently, the successful application of facial image recognition analysis and understanding has received significant attention. However, there are still many challenges in implementing a face recognition system in the real world. One of the main challenges faced is the "curse of dimensionality" problem, a pervasive problem in pattern recognition. It points to the fact that, at the cost of increasing data size, the sample estimates required to accurately represent the data grow exponentially. The spatial resolution of face images is at least hundreds of pixels, sometimes tens of thousands. From a statistical point of view, this will require tens of thousands of face samples to deal with the problem of face ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/66
Inventor 徐昕安向京郑睿左磊李健
Owner NAT UNIV OF DEFENSE TECH
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