Face recognition feature extraction algorithm based on local cyclic graph structure

A face recognition and feature extraction technology, applied in the field of image processing, can solve the problems of lack of information and unsatisfactory face recognition effect, and achieve the effect of improving the recognition rate and effective face recognition

Inactive Publication Date: 2017-11-17
TIANJIN UNIV OF SCI & TECH
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Problems solved by technology

[0004] However, LGS and MOW-SLGS respectively take 3×4 and 5×5 neighborhoods when performing neighborhood division, but when calculating eigenvalues, they do not use all the information of surrounding pixels in the neighborhood, resulting in information loss. As a result, the final face recognition effect is not ideal.

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  • Face recognition feature extraction algorithm based on local cyclic graph structure
  • Face recognition feature extraction algorithm based on local cyclic graph structure
  • Face recognition feature extraction algorithm based on local cyclic graph structure

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Embodiment Construction

[0024] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0025] A face recognition feature extraction algorithm based on a local cycle graph structure, comprising the following steps:

[0026] Step 1: In a face grayscale image, select a 3×3 neighborhood, such as figure 1 shown.

[0027] In this embodiment, the selected neighborhood (nine pixels from left to right and from top to bottom: X 1 、X 2 、X 3 、X 8 、X 0 、X 4 、X 5 、X 6 、X 7 ) pixel values ​​are 8, 18, 26, 13, 22, 28, 21, 31, 20 respectively, such as image 3 shown.

[0028] Step 2: Cycle through the selected neighborhood to construct the graph structure, such as figure 2 shown.

[0029] In this embodiment, the graph structure constructed by loop is as follows Figure 4 As shown, the order of the graph structure is X 0 →X 8 →X 1 →X 0 →X 2 →X 3 →X 0 →X 4 →X 5 →X 0 →X 6 →X 7 →X 0 .

[0030] Step 3: According to the order o...

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Abstract

The invention relates to a face recognition feature extraction algorithm based on a local cyclic graph structure. The algorithm is mainly characterized by selecting a 3 * 3 neighborhood in a face grayscale image and setting corresponding pixel values; cyclically constructing a graph structure in the selected neighborhood in a certain order; successively comparing the pixel values according to the order of the graph structure along the direction of an arrow, and expressing a result in binary; and calculating the characteristic value of the central pixel in the neighborhood. The algorithm is reasonable in design, can accurately describe facial features, provides the obtained characteristic value of the target pixel with more representativeness, and improves a recognition rate. The obtained characteristic value describes the characteristic information of a facial image comprehensively so as to be capable of face recognition. Further, the algorithm has advantages over a symmetrical local graph structure algorithm based on multi-directional weight optimization in terms of time consumption.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a face recognition feature extraction algorithm (LCGS) based on a local cycle graph structure. Background technique [0002] A complete face recognition system includes three modules: object detection, feature extraction and face recognition. Among them, whether effective facial feature information can be extracted is a very important step. There are many algorithms for extracting facial features, which can be roughly divided into two types based on global features and based on local features. Studies have shown that the extraction method based on local features is slightly higher than the extraction method based on global features in terms of recognition rate. In 2011, inspired by the dominating set in the graph structure, Abusham et al. applied it to face feature extraction and proposed the local graph structure operator (LGS). This operator extracts 5 pixels around...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V40/168
Inventor 杨巨成张灵超赵婷婷陈亚瑞张传雷刘建征韩书杰胡志强孙文辉李梦
Owner TIANJIN UNIV OF SCI & TECH
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