[1]刘悦婷,张燕.CMSFLA-SVM算法在人脸识别中的应用[J].延边大学学报(自然科学版),2015,41(04):337-342.
　LIU Yueting,ZHANG Yan.Application of CMSFLA-SVM algorithm in face recognition[J].Journal of Yanbian University,2015,41(04):337-342.
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CMSFLA-SVM算法在人脸识别中的应用

《延边大学学报(自然科学版)》[ISSN:1004-4353/CN:22-1191/N] 卷: 第41卷 期数: 2015年04期 页码: 337-342 栏目: 应用科学研究 出版日期: 2015-12-20
Title:
Application of CMSFLA-SVM algorithm in face recognition
文章编号:
1004-4353(2015)04-0337-06
作者:
刘悦婷; 张燕
兰州文理学院 电子信息工程学院, 甘肃 兰州 730000
Author(s):
LIU Yueting;  ZHANG Yan
School of Electronics and Information Engineering, Lanzhou University of Arts and Science, Lanzhou 730000, China
关键词:
支持向量机;  蛙跳算法;  交叉;  变异;  人脸识别;  最优解
Keywords:
support vectors machines(SVM);  shuffled frog leaping algorithm(SFLA);  crossover;  mutation;  face recognition;  optimal solution
分类号:
TP391
文献标志码:
A
摘要:
针对蛙跳算法(shuffled frog leaping algorithm,SFLA)易陷入局部最优,且求解精度较低的问题,提出一种交叉变异的蛙跳算法(crossover and mutation shuffled frog leaping algorithm,CMSFLA).该算法在全局搜索中,青蛙个体依适应度值而选择不同概率分别进行交叉和变异操作.将改进的蛙跳算法CMSFLA训练支持向量机(support vectors machines,SVM),并将其用于人脸识别中.ORL和CAS-PEAL-R
Abstract:
Because of shuffled frog leaping algorithm(SFLA)such as local optimal and low precision solution,a crossover and mutation shuffled frog leaping algorithm is presented. In the global search, each of frog individuals according to the fitness will choose different probability of crossover and mutation operations. A face recognition algorithm using CMSFLA to train SVM is proposed. The simulation results of experiments on the ORL and CAS-PEAL-R1 face database show that compared with ASFLA-SVM and KSFLA-SVM, CMSFLA-SVM has higher recognition rate and higher speed. In the lack of training samples, CMSFLA-SVM also has good recognition effect.

参考文献/References:

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相似文献/References:

[1]刘悦婷.基于近邻密度改进的SVM不平衡数据集分类算法[J].延边大学学报(自然科学版),2018,44(01):43.
　LIU Yueting.Imbalanced dataset classification algorithm based on NDSVM[J].Journal of Yanbian University,2018,44(04):43.

备注/Memo

基金项目: 甘肃省高等学校科研项目(2015B-132)
作者简介: 刘悦婷(1979—),女,副教授,研究方向为电子、自动控制.

更新日期/Last Update: 2015-12-20