[1]孙劲光,孟凡宇.一种特征加权融合人脸识别方法[J].智能系统学报编辑部,2015,10(6):912-920.[doi:10.11992/tis.201509025]
 SUN Jinguang,MENG Fanyu.Face recognition by weighted fusion of facial features[J].CAAI Transactions on Intelligent Systems,2015,10(6):912-920.[doi:10.11992/tis.201509025]
点击复制

一种特征加权融合人脸识别方法

参考文献/References:
[1] TURK M, PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86.
[2] SUN Yi, WANG Xiaogang, TANG Xiaoou. Deep learning face representation from predicting 10, 000 classes[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1891-1898.
[3] HU Junlin, LU Jiwen, TAN Y P. Discriminative deep metric learning for face verification in the wild[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1875-1882.
[4] HUANG G B, LEE H, LEARNED-MILLER E. Learning hierarchical representations for face verification with convolutional deep belief networks[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2012:2518-2525.
[5] ZHU Zhenyao, LUO Ping, WANG Xiaogang, et al. Deep learning identity-preserving face space[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013:113-120.
[6] SUN Yi, WANG Xiaogang, TANG Xiaoou. Hybrid deep learning for face verification[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013:1489-1496.
[7] TAIGMAN Y, YANG Ming, RANZATO M A, et al. Deepface:Closing the gap to human-level performance in face verification[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1701-1708.
[8] SUN Yi, CHEN Yuheng, WANG Xiaogang, et al. Deep learning face representation by joint identification-verification[J]. Advances in Neural Information Processing Systems. 2014.
[9] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[10] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[11] AREL I, ROSE D C, KARNOWSKI T P. Deep machine learning a new frontier in artificial intelligence research[J]. IEEE Computational Intelligence Magazine, 2010, 5(4):13-18.
[12] COOTES T F, TAYLOR C J, COOPER D H, et al. Active shape models-their training and application[J]. Computer Vision and Image Understanding, 1995, 61(1):38-59.
[13] MILBORROW S, NICOLLS F. Active shape models with SIFT descriptors and MARS[J]. VISAPP, 2014, 1(2):5.
[14] MILBORROW S, BISHOP T E, NICOLLS F. Multiview active shape models with SIFT descriptors for the 300-W face landmark challenge[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops. Sydney, NSW, Australia, 2013:378-385.
[15] Bengio Y, Delalleau O. On the expressive power of deep architectures[C]//Proceedings of the 22nd International Conference. Espoo, Finland, 2011:18-36.
[16] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8):1771-1800.
[17] BENGIO Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2(1):1-127.
相似文献/References:
[1]金一,阮秋琦.一种局部加权的二维主成分分析算法及其在人脸识别中的应用[J].智能系统学报编辑部,2007,2(3):25.
 JIN Yi,RUAN Qiu-qi.A partially weighted twodimensional PCA for face recognition[J].CAAI Transactions on Intelligent Systems,2007,2():25.
[2]任小龙,苏光大,相 燕.使用第2代身份证的人脸识别身份认证系统[J].智能系统学报编辑部,2009,4(3):213.
 REN Xiao-long,SU Guang-da,XIANG Yan.Face authentication system using the Chinese second generation identity card[J].CAAI Transactions on Intelligent Systems,2009,4():213.
[3]苏光大.多技术合力的人脸识别系统设计[J].智能系统学报编辑部,2009,4(6):471.[doi:doi:10.3969/j.issn.1673-4785.2009.06.001]
 SU Guang-da.Face recognition system designed to integrate multiple techniques[J].CAAI Transactions on Intelligent Systems,2009,4():471.[doi:doi:10.3969/j.issn.1673-4785.2009.06.001]
[4]王科俊,邹国锋,张洁.SPCA参数对单样本人脸识别效果影响分析[J].智能系统学报编辑部,2011,6(6):531.
 WANG Kejun,ZOU Guofeng,ZHANG Jie.Analysis of the influence of SPCA parameters on the recognition of a single sample face[J].CAAI Transactions on Intelligent Systems,2011,6():531.
[5]杜吉祥,翟传敏,叶永青.使用稀疏约束非负矩阵分解算法的跨年龄人脸识别[J].智能系统学报编辑部,2012,7(3):271.
 DU Jixiang,ZHAI Chuanmin,YE Yongqing.An agespan face recognition method based on an NMF algorithm with sparseness constraints[J].CAAI Transactions on Intelligent Systems,2012,7():271.
[6]阮晓虎,李卫军,覃鸿,等.一种基于特征匹配的人脸配准判断方法[J].智能系统学报编辑部,2015,10(1):12.[doi:10.3969/j.issn.1673-4785.201312064]
 RUAN Xiaohu,LI Weijun,QIN Hong,et al.An assessment method for face alignment based on feature matching[J].CAAI Transactions on Intelligent Systems,2015,10():12.[doi:10.3969/j.issn.1673-4785.201312064]
[7]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报编辑部,2016,11(3):279.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[8]刘训利,龚勋,王国胤.一种基于非残差估计线性表示模型的人脸识别[J].智能系统学报编辑部,2014,9(3):285.[doi:10.3969/j.issn.1673-4785.201309065]
 LIU Xunli,GONG Xun,WANG Guoyin.Face recognition based on the linear representation model without residual estimation[J].CAAI Transactions on Intelligent Systems,2014,9():285.[doi:10.3969/j.issn.1673-4785.201309065]
[9]夏洋洋,龚勋,洪西进.人脸识别背后的数据清理问题研究[J].智能系统学报编辑部,2017,12(5):616.[doi:10.11992/tis.201706025]
 XIA Yangyang,GONG Xun,HONG Xijin.Research on the data cleansing problem for face recognition technology[J].CAAI Transactions on Intelligent Systems,2017,12():616.[doi:10.11992/tis.201706025]
[10]余拓,陈莹.基于加权边缘弱化引导滤波的人脸光照补偿[J].智能系统学报编辑部,2018,13(3):373.[doi:10.11992/tis.201612011]
 YU Tuo,CHEN Ying.Face illumination compensation based on weighted edge-weakening guided image filter[J].CAAI Transactions on Intelligent Systems,2018,13():373.[doi:10.11992/tis.201612011]

备注/Memo

收稿日期:2015-09-17;改回日期:。
基金项目:国家科技支撑计划资助项目(2013BAH12F02).
作者简介:孙劲光,女,1962年生,博士,教授,博士生导师,计算机学会(CCF)会员(21314S),主要研究方向为计算机图像处理、计算机图形学、知识工程。孟凡宇,男,1991年生,硕士研究生,主要研究方向为计算机图像处理。
通讯作者:孟凡宇.E-mail:mengfanyu1991@163.com.

更新日期/Last Update: 1900-01-01
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134