A Hog and Gabor feature efficient fusion fast face recognition method based on near-infrared face image

A technology of face recognition and infrared human beings, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as low efficiency

Inactive Publication Date: 2019-02-22
DALIAN UNIV OF TECH
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Problems solved by technology

Although the multi-feature fusion method improves the efficiency of face recognition to a certain extent, the feature fusion process is also prone to generate new matrices with too high dimensions, resulting in low efficiency in the subsequent classification and recognition process.

Method used

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  • A Hog and Gabor feature efficient fusion fast face recognition method based on near-infrared face image
  • A Hog and Gabor feature efficient fusion fast face recognition method based on near-infrared face image
  • A Hog and Gabor feature efficient fusion fast face recognition method based on near-infrared face image

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Embodiment Construction

[0073] The specific embodiments of the present invention will be described in detail below in conjunction with the technical solution and the drawings.

[0074] Step 1: Perform Hog feature extraction on the near-infrared face image training samples:

[0075] First, perform image gray-scale and color space normalization processing on the near-infrared face image training samples. The color space normalization processing formula is: L(x 1 ,y 1 )=E(x 1 ,y 1 ) γ , Where L(x 1 ,y 1 ) Is the color space normalized image at the pixel point (x 1 ,y 1 ), E(x 1 ,y 1 ) Is the image at pixel point (x 1 ,y 1 ), the gray value at

[0076] Then, calculate the pixel point (x 1 ,y 1 ) Of the horizontal gradient G x (x 1 ,y 1 ) And vertical gradient G y (x 1 ,y 1 ):

[0077] G x (x 1 ,y 1 )=L(x 1 +1,y 1 )-L(x 1 -1,y 1 )

[0078] G y (x 1 ,y 1 )=L(x 1 ,y 1 +1)-L(x 1 ,y 1 -1)

[0079] Furthermore, the gradient size G(x 1 ,y 1 ) And the gradient direction θ(x 1 ,y 1 ) Are:

[0080]

[0081]

[0082] After th...

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Abstract

The invention provides a Hog and Gabor feature efficient fusion fast face recognition method based on near-infrared face image, belonging to the technical field of pattern recognition and image processing. The method includes Hog feature extraction and Gabor feature extraction for near-infrared face image samples respectively. The non-negative matrix decomposition method is used to reduce the dimension of two kinds of eigenmatrices respectively. The Hog feature and Gabor feature are fused to obtain the fused feature. The linear discriminant analysis (LDA) was used to reduce the dimension of the fusion feature matrix, and the feature vectors of the training samples after the transformation matrix of the second projection and the second reduction are obtained. The nearest neighbor algorithmKNN is used to classify and recognize the test samples based on the distribution of training samples. The invention ensures the comprehensiveness of acquiring characteristic information, effectively improves the characteristic representation efficiency, reduces the cost of algorithm operation storage and time consumption, and improves the efficiency of face recognition process.

Description

Technical field [0001] The invention belongs to the technical field of pattern recognition and image processing, and specifically relates to a fast face recognition method based on Hog ​​and Gabor features of near-infrared face images. Background technique [0002] As an important biometric identification method, face recognition has extremely high research value in the field of scientific research. Face recognition is one of the important fields of artificial intelligence, involving many research fields such as image processing, pattern recognition, and computer vision. At present, its development and application have become the focus and hotspot of research by domestic and foreign researchers. [0003] The research on face recognition generally includes the following five aspects: image acquisition, face detection and feature point positioning, face standardization, face feature extraction, classification and recognition. Among them, feature extraction is the most important link...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V40/161G06V40/172G06V40/168
Inventor 刘惠吴佳霖张怡然王金柯
Owner DALIAN UNIV OF TECH
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