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Facial image dimensionality reduction classification method based on two-dimensional principal component analysis

A two-dimensional principal component and face image technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of missing information, achieve accurate calculation, fast calculation of eigenvectors, and improved face recognition rate Effect

Inactive Publication Date: 2013-04-03
JINLING INST OF TECH
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[0005] However, from the covariance matrix calculated by the two methods of PCA and 2DPCA, the covariance matrix obtained by the 2DPCA method is smaller than the PCA method, and only uses the diagonal elements of the covariance matrix of the PCA method, obviously missing many information, if the 2DPCA method can utilize more information, the recognition rate of the method will be improved

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  • Facial image dimensionality reduction classification method based on two-dimensional principal component analysis
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  • Facial image dimensionality reduction classification method based on two-dimensional principal component analysis

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[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like figure 1 As shown, the face image dimensionality reduction method based on the two-dimensional principal component analysis method of the present invention includes the following steps:

[0026] Step S101 : take a grayscale image of a face image training sample with a resolution of h×w, where h and w represent the number of rows and columns of the face image matrix, respectively. In this example, the face images in the ORL face database are taken. The ORL face database is the face image data collected in the laboratory by the University of Cambridge from 1992 to 1994. composition of images. All images in the ORL face database are scaled to a resolution of 112*92 and a gray level of 256. Among the 10 face image samples of each person, 5 face avatars are randomly selected as training samples. The selected face samples are a...

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Abstract

The invention discloses a facial image dimensionality reduction classification method based on two-dimensional principal component analysis. The method comprises the steps of: firstly, performing image transformation on training samples; secondly, working out a covariance matrix according to transformed image data by using a 2DPCA (Data Processing Control Area) method, and working out the best projection matrix of a population covariance matrix; thirdly, performing spacial dimensionality reduction on projection of the training samples on the best projection matrix; and finally, classifying the training samples according to a nearest rule in a low-dimensional space. The method disclosed by the invention is quick to calculate a feature vector, accurate in calculation and high in recognition rate.

Description

technical field [0001] The invention relates to dimension reduction and classification in face image recognition, in particular to a face image dimension reduction method based on two-dimensional principal component analysis. Background technique [0002] The first step in face image recognition is dimensionality reduction, followed by classification. Because classification in high-dimensional space will lead to increased computational complexity and poor data visibility, and the robustness of statistical methods with good robustness in low-dimensional space is also deteriorated when applied to high-dimensional space. [0003] Because the face matrix is ​​a high-dimensional matrix, it is necessary to reduce its dimensionality. Currently used face recognition dimensionality reduction methods generally use PCA (Principal Component Analysis, principal component analysis) and 2DPCA (two-dimensional Principal Component Analysis, two-dimensional principal component analysis). PC...

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
Inventor 曾岳吴巧熊莉黄业磊
Owner JINLING INST OF TECH
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