Face recognition method based on structuralized factor analysis

A factor analysis and face recognition technology, applied in the field of image processing, can solve the problems of ignoring the global distribution structure, destroying the local neighbor structure, low face recognition accuracy, etc., to overcome the poor stability, improve the face recognition rate, The effect of high face recognition rate

Active Publication Date: 2014-12-24
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that in the feature extraction process, only non-negativity constraints are imposed on the feature projection matrix and the low-dimensional features of the training image, and the local clustering characteristics and global distribution structure of the face image in the high-dimensional feature space are ignored. , resulting in relatively low accuracy of face recognition
The disadvantage of this method is that in the process of featur...

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  • Face recognition method based on structuralized factor analysis
  • Face recognition method based on structuralized factor analysis
  • Face recognition method based on structuralized factor analysis

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[0032] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0033] combined with figure 1 A detailed description of the steps implemented by the present invention is as follows.

[0034] Step 1, divide the face image dataset.

[0035] The face images to be recognized are extracted from the face image database, and all pixels of each face image to be recognized are combined into a vector to obtain a feature vector of each face image to be recognized.

[0036] Divide the feature vector of each face image to be recognized by the modulus of the feature vector to obtain normalized sample data.

[0037] Randomly select 50% of all normalized sample data as training samples, and use the remaining 50% of normalized sample data as test samples.

[0038] Step 2, perform cluster analysis.

[0039] The principal component analysis method is used to perform initial dimensionality reduction on all training sample data to obtain ...

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Abstract

The invention discloses a face recognition method based on structuralized factor analysis. The problem that the face recognition accuracy rate is low due to the fact that local clustering features and a global distribution structure of face image data cannot be kept in the prior art is mainly solved. The face recognition method comprises the achieving steps of 1 dividing a face image data set; 2 performing clustering analysis on all training sample data; 3 calculating an optimal feature projection matrix through Gibbs sampling; 4 extracting low-dimension features of all test sample data and the training sample data; 5 recognizing a face image. The face recognition method synthesizes the local clustering features and the global distribution structure of the face image data and improves the face recognition accuracy rate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a face recognition method based on structural factor analysis in the technical field of pattern recognition and machine learning. The invention can be applied to identity recognition and information security, and improves the accuracy of face recognition by extracting face features with fewer dimensions. Background technique [0002] Face recognition technology is a computer technology that uses computers to analyze face images and extract effective visual feature information for identity identification. Among the existing biometric identification technologies, face recognition technology has been widely used due to its advantages of simple operation and easy implementation. The dimensionality of face images is usually high, and there are strong similarities between different face images. If only the original face images are used for identity identification, the f...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 高新波刘卫芳王秀美高宪军邓成田春娜王颖牛振兴韩冰
Owner XIDIAN UNIV
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