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Improved compressed sensing-based face recognition method

A technology of face recognition and compressed sensing, which is applied in the field of improved compressed sensing face recognition, can solve the problems of complex calculation of norm minimum solution, huge amount of calculation, and decrease of calculation speed, etc., and achieves the preservation of local texture characteristics and good statistical similarity The effect of improved performance and calculation speed

Inactive Publication Date: 2016-07-27
SHANDONG NORMAL UNIV
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Problems solved by technology

but The calculation of the norm minimum solution is complex and the amount of calculation is huge, and as the number of dictionary atoms increases, the calculation speed drops sharply, resulting in slow recognition speed, which is not suitable for real-time applications

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Embodiment Construction

[0037] The present invention is described in detail below in conjunction with accompanying drawing:

[0038] 1. Local Binary Pattern (LBP) Coding Principle

[0039] A pair of values ​​(P, R) is usually used to describe (x c ,y c ) as the center and R as the radius of the local circular neighborhood, such as figure 1 shown. use g c Represents the gray value of the central pixel, g p (p=0,...,P-1) represents the gray value of P neighboring pixels equally spaced on the circumference. Assuming that the coordinates of the central pixel are (0,0), the coordinates of the neighboring pixels (x p ,y p ) is (-Rsin(2πp / P), Rcos(2πp / P)). If (x p ,y p ) does not fall on the pixel point, then use the bilinear interpolation method to calculate the gray value g of the point p . Different value combinations of P and R describe different LBP descriptors.

[0040] The LBP algorithm uses the idea of ​​structure to analyze the texture features of the image in the fixed window. Take t...

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Abstract

The invention discloses an improved compressed sensing-based face recognition method. The method includes the following steps that: step 1, image pre processing is carried out: a cumulative distribution function is adopted to perform histogram equalization on a gray face image, and then, gray face image feature vectors extracted by an invariant uniform pattern are rotated through adopting an LBP (local binary pattern) so as to construct an LBP feature space; step 2, neighbor samples of a test image are searched in the low-dimensional LBP feature space; and step 3, the neighboring samples are utilized to adaptively construct a redundant dictionary, and the sensing recognition of the test image is completed. According to the method, the neighbor samples of the test sample are searched in the low-dimensional LBP feature space, and are adopted to form the compete redundant dictionary, and therefore, the number of the atoms of the dictionary is greatly decreased, and at the same time, the atoms of the dictionary have higher structural similarity with the test sample, and thus, the algorithm can not only improve recognition speed, but also can improve correct recognition rate.

Description

technical field [0001] The invention discloses an improved compression sensing face recognition method. Background technique [0002] Human facial features have become a new identity authentication medium because of their advantages of uniqueness, stability and non-stealing. Compared with other biometric identification, the face recognition method is more in line with human's own habit of identifying identity, the recognition is more natural and intuitive, the face image acquisition equipment is simple, and it can be completed by ordinary household cameras under natural light, and no The user cooperates and even has concealment, thus reducing the probability of camouflage and deception. The human face will not easily leave traces on the medium, which is more secure. Therefore, the application prospect of face recognition technology in the fields of military security, public security, civil affairs and economy is very broad. Because the face is easily affected by factors s...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/161G06V40/172G06V10/507G06F18/24147
Inventor 魏冬梅周茂霞马娜
Owner SHANDONG NORMAL UNIV
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