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Face recognition method based on particle swarm optimization bp network

A technology of particle swarm optimization and BP network, which is applied in the field of face recognition, can solve problems affecting the convergence stability of the algorithm, and achieve the effect of avoiding falling into local extremum, improving efficiency and accuracy, and speeding up the optimization ability

Active Publication Date: 2017-08-15
WINGTECH COMM
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Problems solved by technology

In the evolution process of the particle swarm algorithm, it is required that in the initial stage of the algorithm, the inertia weight w should be selected to a larger value to speed up the convergence speed of the algorithm; Small, otherwise the particles will fly out of the vicinity of the optimal solution and fail to converge, which will affect the stability of the algorithm convergence

Method used

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  • Face recognition method based on particle swarm optimization bp network
  • Face recognition method based on particle swarm optimization bp network
  • Face recognition method based on particle swarm optimization bp network

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Embodiment 1

[0037] see Figure 6 , the present invention discloses a face recognition method based on particle swarm optimization BP network, said method comprising:

[0038] The image is preprocessed to remove external interference and provide high-quality images for subsequent processing; the preprocessed image information is projected into the feature space by selecting different feature extraction methods through mapping transformation to form an m×n Matrix, each parameter corresponds to a feature; during the training or recognition process of the neural network, each feature corresponds to an input node of the neural network, and the output node is equal to the number of categories, and an output node corresponds to a class;

[0039] Thus, a fully connected BP network is designed, in which the number of neurons in the input layer corresponds to the number of features of the image, the number of neurons in the output layer corresponds to the number of population categories, and the nu...

Embodiment 2

[0055] Face recognition technology is a kind of biometric technology. It has great application value in human-computer interaction, identity authentication, video communication, etc. It is a difficult research field that has broad application prospects and has made great progress. . Among the main face recognition methods based on geometric features, eigenfaces, elastic templates and neural networks, the neural network has been used in face recognition because of its fast convergence speed, compact topology, and structural parameters that can be learned separately. Wide range of applications.

[0056] 1. Face recognition system

[0057] The use of BP neural network for face recognition requires preprocessing of the input image, image feature extraction and then BP network training. After the network is trained, the trained network is used for image recognition.

[0058] A complete face recognition system such as figure 1 As shown, the image is pre-processed to remove or ext...

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Abstract

The invention discloses a face recognition method based on particle swarm optimization BP network. The image is pre-processed to remove external interference; the pre-processed image information is projected to the Feature space; in the process of neural network training or recognition, each feature corresponds to an input node of the neural network, and the output node is equal to the number of categories, and one output node corresponds to one class; thus a fully connected BP network is designed, where The number of neurons in the input layer corresponds to the number of features of the image, the number of neurons in the output layer is the number of population categories, and the number of neurons in the hidden layer is set to The network weights are initialized to random values ​​between [0,1], and each particle corresponds to a neural network. The present invention adjusts the inertial weight of the particles in real time according to the particle fitness value and the change amount of the particle fitness value, can quickly find the global optimal solution, and finally improves the efficiency and accuracy of face recognition.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and relates to a face recognition method, in particular to a face recognition method based on particle swarm optimization BP network. Background technique [0002] The BP algorithm is used in the smile recognition of mobile phone cameras now. BP algorithm is a heuristic algorithm, the algorithm runs slowly, and it is easy to fall into local extremum, and the optimization effect is poor. [0003] Particle swarm optimization algorithm is a swarm intelligence algorithm that simulates birds looking for food and human activities. It is a global random optimization algorithm. The particle swarm optimization algorithm has a strong global optimization ability, and the algorithm is simpler to implement, and the optimization performance is very stable. It is more and more used in the problem solving of traditional heuristic algorithms. [0004] Particle swarm algorithm is the particle to its own...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/66G06N3/08
Inventor 李保印
Owner WINGTECH COMM
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