摘要
为了提高最大间距准则法表征人脸特征空间的能力,提出了一种融合最大间距准则和二进制粒子群优化算法的人脸识别方法。利用离散二进制粒子群算法对最大间距准则变换后的特征向量进行选择优化,获得有利于分类的最优特征子空间。在ORL和Yale标准人脸库上的实验结果表明,该方法不但降低了特征空间的维数,而且更好的发挥了最大间距准则算法的优点,提高了人脸识别的速度和精度。
In order to improve the ability of Maximum Margin Criterion(MMC),a face recognition method which fusion the MMC and binary particle pwarm optimization algorithm(BPSO)is proposed.Then BPSO is used in feature selection after the transformation of MMC,which can find out feature subspace that is beneficial to classification.Experimental results on ORL face database and Yale face database show that the proposed method not only reduces the dimensions of face feature space,but also expresses the advantages of MMC,improves the speed and accuracy of face recognition.
出处
《科学技术与工程》
北大核心
2012年第15期3640-3644,共5页
Science Technology and Engineering
基金
陕西省教育厅科研计划项目(11JK0512
11JK0517)
商洛学院科研基金项目(11SKY003)
商洛学院教育教学改革项目(10jyjx02006)资助
关键词
人脸识别
最大间距准则
二进制粒子群优化
特征提取
face recognition smaximum margin criterion binary particle pwarm optimization feature extraction