An Adaptive Redundant Dictionary Construction Method for Face Recognition

A redundant dictionary, face recognition technology, applied in the field of face recognition, can solve the problems of lack of self-adaptation and reduced calculation speed, and achieve the effect of improving the correct recognition rate, improving the solution speed, and improving the recognition speed.

Active Publication Date: 2019-03-19
SHANDONG NORMAL UNIV
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AI Technical Summary

Problems solved by technology

First, increase the number of atoms to increase the sparsity of the representation vector to improve the recognition rate, but the calculation speed of any sparse solution algorithm will decrease as the number of atoms increases
Second, for any input test image, the redundant dictionary is fixed and not adaptive

Method used

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  • An Adaptive Redundant Dictionary Construction Method for Face Recognition
  • An Adaptive Redundant Dictionary Construction Method for Face Recognition
  • An Adaptive Redundant Dictionary Construction Method for Face Recognition

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Embodiment Construction

[0032] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

[0033] 1. Local tectonic pattern (LCP) features

[0034] The LBP feature of the present invention is obtained by binarizing the gray level difference between adjacent pixels and central pixels in the neighborhood, which enhances the robustness of illumination changes, but weakens the interrelationship between pixels, thereby losing local structure information.

[0035] In order to overcome this problem, the present invention adopts local configuration pattern (LCP) coding, and divides image features into two layers. The first layer uses LBP to extract local structure information, which is called LBP feature, and the second layer is called Microscopic Configuration (MiC) feature, which is used to describe the linear relationship between adjacent pixels. The extraction method is described as follows.

[0036] The Local Binary Pattern (LBP) can capture the deta...

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Abstract

The invention discloses an adaptive redundant dictionary construction method for face recognition, including acquiring a face image to be tested and several face image samples, storing several face image samples into a training sample set, and extracting a face image to be tested and the LCP feature of each sample in the training sample set; obtain the average LBP feature similarity and the average MiC feature similarity of all samples in the face image to be tested and the training sample set; judge whether the LBP feature and the MiC feature all belong to legal features, if so, Then enter the next step; otherwise, refuse to identify the face image to be tested; in the legal feature, when the LBP feature similarity is not less than the LBP feature neighbor threshold and the MiC feature similarity is not greater than the MiC feature neighbor threshold, the corresponding sample in the training sample set is marked as the neighbor sample of the face image to be tested; the marked neighbor samples of the face image to be tested are used as atoms to construct a redundant dictionary for adaptive face recognition.

Description

technical field [0001] The invention belongs to the field of face recognition, and in particular relates to an adaptive redundant dictionary construction method for face recognition when a classification-based sparse representation algorithm is adopted. Background technique [0002] Face recognition is a technology that uses people's facial images for identity recognition, and has broad application prospects in economic, civil, military, public security and other fields. Sparse representation utilizes an over-complete redundant dictionary to perform an optimal linear representation of the input test data, and the representation coefficients are sparse vectors. Classification-based sparse representation algorithms are a class of algorithms that use sparse representation vectors and various training samples to reconstruct and classify test data class by class. A large number of literatures show that classification-based sparse representation algorithms have a high correct rec...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/168
Inventor 魏冬梅赵曰峰马娜潘杰
Owner SHANDONG NORMAL UNIV
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