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Face recognition based on curvelet transform and independentcomponent analysis(PDF)


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Face recognition based on curvelet transform and independentcomponent analysis
Zhang LinmeiZhang Xuefeng
College of Information Engineering,Xinyang College of Agriculture and Forestry,Xinyang 464000,China
face recognition feature extraction curvelet transform independent component analysis wavelet transform
In order to obtain high recognition rate and accelerate the speed of face recognition,a face recognition algorithm is proposed by combining curvelet transform and independent component analysis.Firstly,face images are processed by curvelet transform and get curvelet coefficients in scale and direction.The obtained curvelet coefficients are weighted and fused and then independent component analysis is used to select the features which have important contributions,reducing the feature dimension to accelerate the recognition speed of face classifier.Finally,least square support vector machine is used to establish face recognition classifier,and the classical face database is used to test the performance of face recognition.The experimental results show that the average recognition rate of the proposed algorithm is more than 95%,and the average recognition time can meet the requirements of face recognition.


[1] 吴巾一,周德龙.人脸识别方法综述[J].计算机应用研究,2009,26(9):3205-3209.
Wu Jinyi,Zhou Delong.Survey of face recognition[J].Application Research of Computers,2009,26(9):3205-3209.
Zhang Zhibin,Lai Jianhuang,Xie Xiaohua,et al.Face recognition based on fusion of high-frequency and low-frequency components[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2013,52(4):1-6.
[3]Zhang J L,Wang Y H,Zhang Z Y,et al.Comparison of wavelet,gabor and curvelet transform for face recognition[J].Optica Applicata,2011,41(1):183-193.
Guo Feng,LiuliLi,Lv Ning.Face reeognition based on LLE and SVM[J].Journal of Jilin University(Information Science Edition),2008,26(1):48-54.
Wang Xinzui,Li Yan,Guo Lihong,et al.Face recognition algorithm based on BD PCA and K NN[J].Journal of PLA University of Science and Technology(Natural Science Edition),2010,11(6):623-238.
Hong Quan,Chen Songcan,Ni Xuelei.Sub-pattern canonical correlation analysis with application in face recognition[J].Acta Automatica Sinica,2008,43(1):21-30.
Fan Chunnian,Zhang Fuyan.Illumination invariant extraction on Gabor phase[J].Journal of Image and Graphics,2012,17(5):676-681.
Zhou Xia,Zhang Hongjie,Wang Xian.Face recognition based on shearlet multi-orientation features fusion and weighted histogram[J].Opto Electronic Engineering,2013,40(7):89-94.
[9]邹建法,王国胤,龚勋.基于增强 Gabor 特征和直接分步线性判别分析的人脸识别[J].模式识别与人工智能,2010,12(4):477-482.
Zou Jianfa,Wang Guoyin,Gong Xun.Face recognition based on enhanced Gabor feature and direct fractional-step linear discriminant analysis[J].Pattern Recognition and Artificial Intelligence,2010,12(4):477-482.
[10]李建科,赵保军,张辉,等.DCT 和 LBP 特征融合的人脸识别[J].北京理工大学学报,2010,30(11):1355-1359.
Li Jianke,Zhao Baojun,Zhang Hui,et al.Fusing DCT and LBP features for face recognition[J].Transactions of Beijing Institute of Technology,2010,30(11):1355-1359.
Zhang Jiangqin,Liao Haibin,Li Yuan.Multi-view face recognition based on factor analysis and sparse representation[J].Computer Engineering and Applications,2013,49(5):154-159.
Huang Pu,Tang Zhenmin.Minimum-distance discriminant projection and its application to face recognition[J].Journal of Image and Graphics,2013,18(2):201-206.
[13]Zhou L J,Liu W Q,Lu Z M,et al.Face recognition based on curvelets and local binary pattern features via using local property preservation[J].Journal of Systems and Software,2014,95(10):209-216.
[14]Wang Keqi,Yang Shaochun,Dai Tianhong,et al.Method of optimizing parameter of least squares support vector machines by genetic algorithm[J].Computer Applications and Software,2009,26(7):109-111.


Last Update: 2017-02-28