基于改进灰狼算法优化支持向量机的人脸识别
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摘要
针对二维主成分分析法(2DPCA)与主成分分析法(PCA)相结合提取人脸特征时效率不高的问题,提出一种2DPCA和快速PCA结合与改进灰狼算法(EGWO)共同优化支持向量机的人脸识别方法。该方法在特征提取方面运用2DPCA与快速PCA相结合,以减少提取特征的维数和提取时间,从而缩短了SVM所需的识别时间。为了提高灰狼算法的全局搜索能力,引用精英反向学习策略初始化种群个体,有效增强GWO的勘探和开采能力,再将其使用到SVM中,迭代获取最佳核参数和惩戒参数,将训练得到的最终分类器应用于人脸识别中。通过6个基准测试函数与GWO和反向学习灰狼算法(OGWO)进行性能比较,改进灰狼算法的收敛精度和收敛速度更优;经ORL和Yale中的人脸图像实验,证明了改进算法相对于GWO、粒子群算法(PSO)和差分进化算法(DE)结合SVM模型的识别结果更佳且稳定性更强。
In order to solve the low efficiency problem of the combined two-dimensional principal component analysis(2 DPCA) and principal component analysis(PCA) in extracting face features, we propose a face recognition method based on the support vector machine optimized by the 2 DPCA and fast PCA combined with an improved gray wolf algorithm(EGWO). Concerning the feature extraction, the method combines the 2 DPCA with fast PCA to reduce the dimension and extraction time of the extracted features, thus reducing the identification time required by the SVM. In order to improve the global search capability of the gray wolf algorithm, the elite opposite-based learning strategy is used to initialize population individuals, which effectively enhances GWO's exploration and mining capabilities. And then the elite opposite-based learning strategy is used in the SVM to iteratively obtain the best kernel parameters and disciplinary parameters, and the final classifier obtained from training is applied in face recognition. The GWO and opposite-based learning grey wolf algorithm(OGWO) are compared on six benchmark test functions in convergence accuracy and speed, and the former outperforms the latter. Experiments on face images in ORL and Yale datasets show that the improved algorithm is better and more stable than the GWO, particle swarm optimization(PSO) and differential evolution(DE) algorithm combined with SVM model.
In order to solve the low efficiency problem of the combined two-dimensional principal component analysis(2 DPCA) and principal component analysis(PCA) in extracting face features, we propose a face recognition method based on the support vector machine optimized by the 2 DPCA and fast PCA combined with an improved gray wolf algorithm(EGWO). Concerning the feature extraction, the method combines the 2 DPCA with fast PCA to reduce the dimension and extraction time of the extracted features, thus reducing the identification time required by the SVM. In order to improve the global search capability of the gray wolf algorithm, the elite opposite-based learning strategy is used to initialize population individuals, which effectively enhances GWO's exploration and mining capabilities. And then the elite opposite-based learning strategy is used in the SVM to iteratively obtain the best kernel parameters and disciplinary parameters, and the final classifier obtained from training is applied in face recognition. The GWO and opposite-based learning grey wolf algorithm(OGWO) are compared on six benchmark test functions in convergence accuracy and speed, and the former outperforms the latter. Experiments on face images in ORL and Yale datasets show that the improved algorithm is better and more stable than the GWO, particle swarm optimization(PSO) and differential evolution(DE) algorithm combined with SVM model.
引文
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[4] Yang Jian,Zhang D.Two-dimensional PCA:A new approach to appearance based face representation and recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):131-137.
[5] Ning zhi-gang.Research on face recognition system based on combination of feature extraction and support vector machine[D].Harbin:Harbin Engineering University,2009.(in Chinese)
[6] Vapnik V.The nature of statistcal learning theory[M].New York:Springer-Verlag,1995:123-179.
[7] Liao Zhou-yu,Wang Yu-ting,Xie xiao-lan,et al.Face recognition by support vector machine based on particle swarm optimization[J].Computer Engineering,2017,43 (12):248-254.(in Chinese)
[8] Mirjalili S,Mirjialili S M,Lewis A.Grey wolf optimizer[J].Adances in Engineering Software,2014,69(3):46-61.
[9] Eswaramoorthy S,Sivakumaran N,Sekaran S.Grey wolf optimization based parameter selection for support vector machines[J].Compel,2016,35(5):1513-1523.
[10] Eid H F,Abraham A.Plant species identification using leaf biometrics and swarm optimization:A hybrid PSO,GWO,SVM model[J].International Journal of Hybrid Intelligent Systems,2018,14(3) 155-165.
[11] Ramakrishnan T,Sankaragomathi B.A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation[J].Pattern Recognition Letters,2017(94):163-171.
[12] Wang Min,Tang Ming-zhu.Novel grey wolf optimization algoritym based on nonlinear convergence factor[J].Application Research of Computers,2016,33(12):3648-3653.(in Chinese)
[13] Zhou Xin-yu,Wu Zhi-jian,Wang Hui,et al.Elite opposi- tion-based particle swarm optimization[J].Acta Electronica Sinica,2013,41 (8):1647-1652.(in Chinese)
[14] Shi Feng,Wang Xiao-chuan,Yu Lei.Matlab neural network 30 cases analysis [M].Beijing:Beihang University Press,2010.(in Chinese)
[3] 张铮,倪红霞,苑春苗,等.精通Matlab数字图像处理与识别[M].北京:人民邮电出版社,2013.
[5] 宁志刚.基于结合特征提取与支持向量机的人脸识别系统研究[D].哈尔滨:哈尔滨工程大学,2009.
[7] 廖周宇,王钰婷,谢晓兰,等.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.
[12] 王敏,唐明珠.一种新型非线性收敛因子的灰狼优化算法[J].计算机应用研究,2016,33(12):3648-3653.
[13] 周新宇,吴志健,王晖,等.一种精英反向学习的粒子群优化算法[J].电子学报,2013,41(8):1647-1652.
[14] 史峰,王小川,郁磊.Matlab神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010.