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Face recognition method based on particle swarm optimization BP network

A particle swarm optimization and BP network technology, applied in the field of face recognition, can solve problems affecting the convergence stability of the algorithm

Active Publication Date: 2014-03-26
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 a particle swarm optimization BP network. The method includes that an image is preprocessed to eliminate external disturbance; information of the preprocessed image is projected to a feature space by means of mapping transformation and by selecting different feature extraction modes; in the training or recognition process of neural networks, each feature corresponds to one input node of each neural network, output nodes are equal to classes in number, and one output node corresponds to one class. Therefore, a fully-connected BP network is designed, wherein the number of neurons in an input layer corresponds to the number of the features of the image, the number of neurons in an output layer is the number of swarm classes, the number of neurons in a hidden layer is set as the following formal, network weight is initialized as a random value between 0 and 1, and each particle corresponds to one neuron network. According to adaptive values of the particles and variable quantities of the adaptive values, inertia weight of each particle is regulated in real time, a global optimal solution can be rapidly found out, and efficiency and accuracy of face recognition are improved finally.

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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IPC IPC(8): G06K9/00G06K9/66G06N3/08
Inventor 李保印
Owner WINGTECH COMM
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