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Human face recognition method based on local binary value and PSO BP neural network

A BP neural network and particle swarm optimization technology, applied to biological neural network models, character and pattern recognition, computer components, etc., can solve the problem that the neural network has no specific principle algorithm, has not been completely solved, and the convergence speed of the neural network has fallen into a local extreme. Small value and other issues

Active Publication Date: 2014-07-16
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

Although the neural network reduces the computational complexity to a certain extent, there is no specific principle and algorithm for setting the parameters of the neural network, and we need to rely on experience to select the value. Problems such as small values ​​have not been completely resolved

Method used

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  • Human face recognition method based on local binary value and PSO BP neural network
  • Human face recognition method based on local binary value and PSO BP neural network
  • Human face recognition method based on local binary value and PSO BP neural network

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Embodiment Construction

[0044] Such as figure 1 , 2 As shown, a face recognition method based on local binary and particle swarm optimization BP neural network includes the following steps:

[0045] Step 1: Randomly select a certain number of each type of face images in the known face database as the training sample set I train ={X 1 ,X 2 ,...,X j ,...,X A}, where X j For each training sample image, A is the number of training samples, and the rest are used as the test sample set I test ;

[0046] Step 2: Perform geometric normalization on each grayscale image of M×N pixels in the training set, and normalize it into an image of H×H size, denoted as I′ train , where 0<H≤min(M,N);

[0047] Step 3: Use the local binary algorithm to normalize the training set image I′ train Extract the illumination invariant, remove the influence of illumination, and obtain the training set image I" after illumination processing train , the process is:

[0048] (1) First, put I' train Each image in is divide...

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Abstract

The invention discloses a human face recognition method based on a local binary value and a PSO BP neural network. The method comprises the steps that first, all kinds of human face images in a known human face library are divided into a training sample set and a testing sample set in a non-overlapped mode, and normalization and local binary preprocessing are conducted on the images; second, two-dimensional discrete wavelet transformation is conducted on the preprocessed images, the influence of diagonal line component is removed, weight fusion is conducted on other three frequency band components, then two-dimensional discrete cosine transform is conducted on the fused images, and a zigzag scanning mode is used for extracting a main transformation coefficient matrix; then, the initial weight value and the threshold value of the PSO BP neural network are used for conducting network training; at last, the data of the testing sample set are sent to the trained BP neural network for testing, and the recognition rate is calculated. According to the human face recognition method based on the local binary value and the PSO BP neural network, high computing efficiency and high recognition capacity are achieved, and the method is suitable for human face recognition systems.

Description

technical field [0001] The invention belongs to a face recognition method, in particular to a face recognition algorithm based on local binary and particle swarm optimization BP neural network, and belongs to the field of intelligent pattern recognition and image processing. Background technique [0002] In recent years, face recognition technology has developed rapidly, and the emergence of a large number of high-performance algorithms has made it move from the laboratory to commercial use. However, so far, face recognition technology is still facing huge challenges: (1) illumination, background, posture, expression, occlusion and age changes; (2) imaging conditions and equipment differences; (3) data scale limitations Wait. Therefore, the problem of high recognition rate in face recognition technology has not been completely resolved. [0003] The change of ambient light is one of the main factors affecting the accuracy of face recognition. It is found that the differen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/02
Inventor 丁欢欢杨永红
Owner JIANGSU UNIV OF SCI & TECH
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