Face feature extraction method based on fusion of improved LTP (Local Ternary Pattern) and two-dimensional bidirectional PCA (Principal Component Analysis)

An extraction method, a technology of facial features, used in instruments, character and pattern recognition, computer parts, etc.

Active Publication Date: 2017-12-12
CHONGQING UNIV OF POSTS & TELECOMM
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However, the above research only considers improving the recognition rate, and the robustness and c

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  • Face feature extraction method based on fusion of improved LTP (Local Ternary Pattern) and two-dimensional bidirectional PCA (Principal Component Analysis)
  • Face feature extraction method based on fusion of improved LTP (Local Ternary Pattern) and two-dimensional bidirectional PCA (Principal Component Analysis)
  • Face feature extraction method based on fusion of improved LTP (Local Ternary Pattern) and two-dimensional bidirectional PCA (Principal Component Analysis)

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[0041] The technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0042] The technical solutions of the present invention to solve the above technical problems are:

[0043] Such as figure 1 As shown, the present invention provides a face feature extraction method based on the fusion of improved LTP and two-dimensional bidirectional PCA, which is characterized in that it includes the following steps:

[0044] S1, divide the face image into non-overlapping sub-areas, J 0 ,J 1 ,J 2 ,...,J t-1 , Where t is the number of non-overlapping regions.

[0045] S2: Establish a difference function equation of the sum of the weights of the center pixel and the neighboring pixels to obtain the statistical characteristics of the center pixel and the neighboring pixels;

[0046] Th...

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Abstract

The invention requests to protect a face feature extraction method based on fusion of an improved LTP (Local Ternary Pattern) and two-dimensional bidirectional PCA (Principal Component Analysis). The method comprises the following steps that: S1: establishing a difference function equation of the weight value sum of a center pixel and a neighbourhood pixel to solve the statistical characteristics of the center pixel and the neighbourhood pixel; S2: establishing an adaptive gradient descent iterative function to calculate a weight coefficient which enable a difference equation to be minimum to define the threshold value of an IALTP (Improve Adaptive Local Ternary Pattern) operator; S3: taking the mean value and the standard difference of local area pixel weight as the coding way of three patterns of the IALTP; S4: solving a projection matrix of a face image in a line and row direction through the two-dimensional bidirectional PCA, and then, establishing joint mapping to obtain the global feature information of the face image; and S5: utilizing a way of fusing local features and global features to put forward an algorithm which fuses the IALTP with the two-dimensional bidirectional PCA. By use of the method, a high identification rate can be obtained, and the method can exhibit high robustness on illumination and random noise.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, in particular to a face feature extraction method based on the fusion of improved LTP and two-dimensional bidirectional PCA. Background technique [0002] With the increasing development of artificial intelligence technology, biometric technology has become a very popular subject in the field of artificial intelligence and pattern recognition. Among them, face recognition is the most representative in terms of practicability and wide application compared with the recognition technology of human inherent physiological characteristics such as iris, fingerprints or acquired characteristics such as voice and gait. Usually the face recognition process is composed of two consecutive stages: the first stage is that the face recognition algorithm is used to extract feature information from a set of training images. The second stage is to extract features by a classifier to identif...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/169G06V40/172G06V10/467G06V10/40G06F18/2135
Inventor 罗元王薄宇张毅
Owner CHONGQING UNIV OF POSTS & TELECOMM
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