计算机工程2017，Vol.43Issue(12)：248-254,7.DOI:10.3969/j.issn.1000-3428.2017.12.045
基于粒子群优化的支持向量机人脸识别
Face Recognition by Support Vector Machine Based on Particle Swarm Optimization
摘要
Abstract
In order to overcome the low efficiency shortcoming of traditional Principal Component Analysis (PCA) feature extraction,this paper proposes a new face recognition method based on fast PCA dimensionality reduction algorithm which is able to accelerate the process of the eigenvalues and eigenvectors of calculating the sample covariance matrix.In the sense of cross-validation,this paper takes the recognition accuracy of Support Vector Machine (SVM) training model as the fitness value of the Particle Swarm Optimization(PSO),searches globally for the optimal values of penalty parameter and kernel function parameter of SVM,obtains the global optimal values of the parameters,and takes the values to train a final classifier model.Experimental results on the face images of ORL and Yale library show that the new method has higher feature extraction efficiency and recognition accuracy compared with the recognition method based on the traditional PCA and SVM algorithm.关键词
特征提取/主成分分析/粒子群优化/人脸识别/支持向量机Key words
feature extraction/Principal Component Analysis (PCA)/Particle Swarm Optimization (PSO)/face recognition/Support Vector Machine (SVM)分类
信息技术与安全科学引用本文复制引用
廖周宇,王钰婷,谢晓兰,刘建明..基于粒子群优化的支持向量机人脸识别[J].计算机工程,2017,43(12):248-254,7.基金项目
广西高校嵌入式技术与智能信息处理重点实验室开放基金(2016-02-20) （2016-02-20）
2016年度广西高校中青年教师基础能力提升项目(KY2016LX285,KY2016YB382) （KY2016LX285,KY2016YB382）
广西高校复杂系统优化与大数据处理重点实验室开放基金(2016CSOBDP0201) （2016CSOBDP0201）
河池学院校级青年科研基金(XJ2015QN007). （XJ2015QN007）
