A Non-adjacent Graph Sparse Face Recognition Method

A face recognition and graph structure technology, applied in the field of sparse representation of face recognition, can solve the problems of sparse graph structure, lack of continuity in data dictionary, not suitable for SRC model, etc., to improve system stability and improve face recognition rate , the effect of improving the face recognition rate

Inactive Publication Date: 2018-09-11
EAST CHINA JIAOTONG UNIVERSITY
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Problems solved by technology

However, the data dictionary of the SRC model does not have such continuity, so the general sparse graph structure is not suitable for the SRC model.

Method used

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  • A Non-adjacent Graph Sparse Face Recognition Method
  • A Non-adjacent Graph Sparse Face Recognition Method
  • A Non-adjacent Graph Sparse Face Recognition Method

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Embodiment Construction

[0037] The present invention is used to improve the face recognition performance of SRC model, and its specific embodiment is, at first constitutes the data dictionary by the training sample of known classification, and the generated data dictionary D∈R n×p Arranged by class; then according to the structure of the data dictionary, use block combination search to generate the base subset space; use the data dictionary, base subset and test set as the input of the structural greedy algorithm, and use the structural greedy algorithm to solve the non-adjacent graph structure Sparse representation coefficient α; finally calculate the nonlinear approximation error of each category for discriminant classification.

[0038] The method of the present invention is verified by some face recognition experiments below. The database uses the AR cropped face database and the extended YaleB face database. There are a total of 2,600 images of 100 people in the AR library, which are divided in...

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Abstract

A non-adjacent graph structure sparse face recognition method, the method includes non-adjacent graph structure sparseness, block combination search, a method for measuring structure sparsity and realizing structure sparse reconstruction. Based on the SRC model, the method enhances the performance of the system through sparse non-adjacent graph structures. The blocks of non-adjacent graph structures are dynamic, overlapping, and unknown in advance, and group members can be different. Adjacent; in order to realize the sparse non-adjacent graph structure, all possible combinations are searched through the combination method to obtain adjacent or non-adjacent blocks; in order to avoid combination explosion in the search, a block combination search method is proposed It is used to limit the search space and generate a computationally feasible base subset space; the method uses a structural greedy algorithm to realize the sparse reconstruction of non-adjacent graph structures. In the algorithm iteration, the base block is selected according to the contribution of the base block. Structural sparsity is measured in encoding complexity. The invention can significantly improve the face recognition rate.

Description

technical field [0001] The invention relates to a non-adjacent graph structure sparse face recognition method, which belongs to the technical field of sparse representation face recognition. Background technique [0002] Compressed sensing (Compressed sensing, CS) aims at signals with sparsity or sparsity in a specific domain, by implementing random sampling far below the Nyquist sampling rate, using the sparsity of the signal and the relationship between the measurement matrix and the measurement basis The incoherence between them can accurately reconstruct the original signal with high probability. Driven by compressive sensing theory, sparse coding and sparse representation technologies have developed rapidly in recent years. The idea of ​​sparse representation is to assume that the observed data y∈R n Can be expressed as a data dictionary D∈R n×p sparse linear combination of , namely: y=Dα, where α∈R p is the representation coefficient of y under the dictionary D. T...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/64G06F17/30
CPCG06F16/958G06V40/16
Inventor 蔡体健谢昕曾德平
Owner EAST CHINA JIAOTONG UNIVERSITY
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