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Research on Face Recognition Algorithm Based on Evolutionary Computation and Support Vector Machine

Author: SunXiangFeng
Tutor: LiMing
School: Lanzhou University of Technology
Course: Applied Computer Technology
Keywords: Face Recognition Evolutionary algorithm Chaos Theory Particle Swarm Optimization Circle mapping Support Vector Machine
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 73
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Abstract


Face recognition is an important branch of the biometrics , information security , criminal detection , access control , and other fields have a wide range of application prospects . The nature of face recognition is a classification problem, the traditional classification method is prone to learning phenomenon advantage because of its excellent learning ability and generalization ability , face recognition has become the preferred classifier , support vector machine . However , when the training sample size is too large , how to solve the contradiction between the training speed training sample size is still the current research focus , good self - adaptive , parallel evolutionary algorithm , can be better to deal with large-scale complex data , attempt to chaos theory and particle swarm algorithm applied to the study of the problem . The main work of this paper include : 1 . Propose a new chaotic model -circle model , experimental results show that the circle model can produce a more uniform distribution of chaotic variable in matlab . 2 particle swarm optimization combined with chaos theory . Basic particle swarm algorithm in the search process , the search space of the particle is a limited area , can not cover the entire space , is easy to fall into local optimum . In this paper, the chaotic system ergodicity and the initial value of the highly sensitive characteristics , chaotic thoughts and particle swarm optimization combined to avoid particle swarm algorithm into a local optimum , and enhance the global search ability of the algorithm . 3 proposed an application of the method of chaotic particle swarm optimization training support vector machines . The slow chaotic particle swarm optimization parallel computing capabilities , solving training large-scale data problem. In the face database , the experimental results show that this method can improve the efficiency of the training support vector machines .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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