Fuzzy two-dimensional uncorrelated discriminant transformation based face recognition method

A non-correlated discrimination, face recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of destroying two-dimensional image data structure, dimensional disaster, etc.

Inactive Publication Date: 2014-03-12
JIANGSU UNIV
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Problems solved by technology

However, the fuzzy non-correlation discriminant transformation method is a one-dimensional linear feature extraction method, which ...

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  • Fuzzy two-dimensional uncorrelated discriminant transformation based face recognition method
  • Fuzzy two-dimensional uncorrelated discriminant transformation based face recognition method
  • Fuzzy two-dimensional uncorrelated discriminant transformation based face recognition method

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0037] Explanation of experimental data: The original face image in the ORL face database of the AT&T Laboratory of the University of Cambridge in the United Kingdom is a two-dimensional grayscale image with a size of 112×92 pixels. The face database includes a total of 40 different people, each with 10 different face images, a total of 400 images. These face images were captured under different conditions of time, different lighting, different head angles, different facial expressions (open / closed eyes, smiling / serious) and different facial details (with or without glasses).

[0038] Step 1, blurring the two-dimensional face image:

[0039] 1. Use the K-nearest neighbor method to obtain a two-dimensional face image sample A k (A k Belonging to the K nearest neighbor s...

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Abstract

The invention discloses a fuzzy two-dimensional uncorrelated discriminant transformation based face recognition method. The method includes: firstly, adopting the fuzzy k-nearest neighbor to realize initial fuzzy processing of face images; secondly, calculating a first discriminant vector of fuzzy two-dimensional uncorrelated discriminant transformation; thirdly, calculating an optimal discriminant vector set of the method; finally, subjecting the two-dimensional face images to fuzzy two-dimensional uncorrelated discriminant transformation so as to realize accurate recognition of faces. By the method, the problem that internal data structures of the images are destroyed due to the fact that the images must be pulled into vectors by line or column during fuzzy two-dimensional uncorrelated discriminant transformation of the two-dimensional face images is solved, the problem of 'curse of dimensionality' caused when the two-dimensional images are pulled to the vectors can be avoided, face discrimination information of the two-dimensional face images can be effectively extracted, and recognition accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and artificial intelligence, in particular to a face recognition method based on fuzzy two-dimensional non-correlation discriminant conversion. Background technique [0002] As a recognition technology based on physiological characteristics in the field of biometric recognition, face recognition technology is a technology that extracts the features of the face through a computer and performs identity verification based on these features. Compared with other biometric identification methods (such as fingerprint recognition, iris recognition, DNA recognition, handwriting recognition, etc.), face recognition has the outstanding advantage of not requiring passive cooperation, and it can collect faces from a long distance, making full use of the established Face database resources can verify their identity more intuitively and conveniently. Its non-contact and non-invasiveness make people ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 武小红孙俊武斌傅海军
Owner JIANGSU UNIV
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