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2DPCA face image recognition method based on bidirectional interpolation enhancement

A face image and two-way interpolation technology, applied in the field of image processing, can solve the problems of large information compression, difficulty in expressing the maximum projection direction of the projection feature vector, etc., and achieve the effect of improving display and computational complexity

Inactive Publication Date: 2019-08-06
HANGZHOU DIANZI UNIV
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Problems solved by technology

The analysis shows that, on the one hand, it is caused by excessive information compression; on the other hand, rows and rows, columns and columns are orthogonal, and there is no redundancy, making it difficult to express the maximum projection direction of the projected feature vector

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  • 2DPCA face image recognition method based on bidirectional interpolation enhancement
  • 2DPCA face image recognition method based on bidirectional interpolation enhancement
  • 2DPCA face image recognition method based on bidirectional interpolation enhancement

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Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Such as image 3 As shown, the present invention has earlier respectively through principal component analysis (PCA), two-dimensional principal component analysis (2DPCA) and two-way two-dimensional principal component analysis ((2D 2 )PCA) to extract eigenvalues ​​and eigenvectors. then use figure 1 The interpolation method shown enhances the eigenvectors, and finally passes the norm distance and figure 2 The support vector machine method shown in the identification, specifically includes the following steps:

[0021] Step 1 divides the ORL face database into training samples and test samples.

[0022] Step 2 extract eigenvalues ​​and eigenvectors

[0023] Step 2.1 Use the PCA method to extract eigenvalues ​​and eigenvectors from the training samples in step 1, the algorithm is as follows:

[0024] Suppose there are face images of N people in a face data...

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Abstract

The invention relates to a2DPCA face image recognition method based on bidirectional interpolation enhancement. The method comprises the steps of firstly dividing an ORL face library into a training sample and a test sample; extracting characteristic values and characteristic vectors from the training samples by using a PCA (Principal Component Analysis) method, a 2DPCA (Two-Dimensional PrincipalComponent Analysis) method and a (2D) 2PCA method respectively; and interpolating the extracted feature vector by using an interpolation method; and finally, performing identifying by adopting a normdistance method and a support vector machine method. According to the method, new vectors are inserted between the high-value feature vectors to expect to improve the display degree of the feature information, and the recognition precision of the image is improved on the premise of not increasing larger calculation complexity.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a 2DPCA human face image recognition method based on bidirectional interpolation enhancement. Background technique [0002] Face recognition is an active research problem in the field of pattern recognition. In the rapidly developing intelligent information age, face recognition plays an increasingly important role. There are many methods for face recognition, principal component analysis (PCA) is one of the main methods to extract eigenfaces. [0003] The principal components extracted by the PCA algorithm are orthogonal to each other, which can eliminate the mutual influence between the original data components, and the idea is simple and easy to implement on the computer. But it needs to convert the image matrix into a one-dimensional vector, resulting in too large covariance matrix dimension, too much calculation, and does not take advantage of the symmetry of the face image. ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T3/40
CPCG06T3/4007G06V40/168G06V40/172G06F18/2135G06F18/2411
Inventor 文成林牛冰川
Owner HANGZHOU DIANZI UNIV
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