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基于局部二元模式和重采样双向2DLDA的人脸识别算法 被引量：1

Face Recognition Based on Local Binary Pattern and Resampling Bidirectional 2DLDA
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摘要 针对人脸识别中存在的小样本问题及光照变化、表情变化等导致的识别问题,提出一种基于局部二元模式和重采样双向2DLDA的人脸识别算法LBP-RB2DLDA.AR人脸库的实验结果表明,该算法具有较高的识别率和鲁棒性. We proposed a face recognition method based on local binary pattern and resampling bidirectional 2DLDA,called LBP-RB2DLDA,to solve the small sample size problem and recognition problem caused by variations in lighting,facial expression,etc.Experiments of AR face database show that the proposed approach possesses high recognition accuracy and robustness.
作者 苏立明 王莹 姜园媛 李文辉
机构地区 长春工业大学基础科学学院 吉林大学计算机科学与技术学院
出处 《吉林大学学报（理学版）》 CAS CSCD 北大核心 2013年第3期459-464,共6页 Journal of Jilin University:Science Edition
基金 国家自然科学基金(批准号:60873147)
关键词 人脸识别 双向2DLDA 重采样 face recognition bidirectional 2DLDA resampling
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献15

  • 1Belhumeur P N, HespanhaJ, Kriegman 0J. Eigenfaces vs Fisherfaces . Recognition Using Class Specific Linear Projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 7l1-720.
  • 2Raudys SJ,Jain A K. Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(3): 252-264.
  • 3HUANG Rui, LIU Qing-shan, LU Han-qing , et a1. Solving the Small Sample Size Problem of LOA[CJ/ / Proceedings of the 16th International Conference on Pattern Recognition. Piscataway: IEEE Press, 2002: 29-32.
  • 4XIONG Hui-lin, Swamy M N S, Ahmad M O. Two-Dimensional FLO for Face Recognition[J]. Pattern Recognition, 2005, 38(7): 1121-1124.
  • 5KONG Hui, WANG Lei, Teoh E K. A Framework of 20 Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples[CJ/ /Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005: 1083-1088.
  • 6Y[Jie-ping,Janardan R, LI Qi. Two-Dimensional Linear Discriminant Analysis[n. Neurocomputing, 2005, 69(1/2/::): 224-23l.
  • 7YANGJian, Zhang 0, YONG Xu, et a1. Two-Dimensional Discriminant Transform for Face Recognition[n. Pattern Recognition, 2005, 38(7): 1125-1129.
  • 8Noushath S, Kumar G H, Shivakumara P. (20)' LOA: An Efficient Approach for Face Recognition[J]. Pattern Recognition, 2006, 39(7): 1369-1400.
  • 9Roth PM, Winter M. Survey of Appearance-Based Methods for Object Recognition[R]. Graz , Graz University of Technology, 2008.
  • 10Ojala T, Pietikainen M, Harwood D. A Comparative Study of Texture Measures with Classification Based on Feature Distributions[J]. Pattern Recognition, 1996, 29(1): 51-59.

二级参考文献20

  • 1P N Belhumeur,J Hespanha,D J Kriegman.Eigenfaces vs.fisherfaces:recognition using class specific linear projection[J].IEEE Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
  • 2Rui Huang,Qingshan Liu,Hanqing Lu.Solving the small sample size problem of LDA .International Conference on Pattern Recognition .Quebec,Canada:IEEE Computer Society,2002.29-32.
  • 3Liu Jun,Chen Songcan,ZhiHua Zhou.Single image subspace for face Recognition .Analysis and Modeling of Faces and Gestures (AMFG'07) .Rio de Janeiro,Brazil:Springer-Verlag Berlin,Heidelberg,2007.205-219.
  • 4Lu Xiaoguang,Anil K Jain.Resampling for face recognition .Audio-and Video-based Biometric Person Authentication (AVBPA'03) .Guilford,UK:Springer-Verlag Berlin,Heidelberg,2003.869-877.
  • 5Liu Jun,Chen Songcan.Resampling LDA/QR and PCA+LDA for face recognition .Artificial Intelligence .Sydney,Australia:Springer-Verlag New York Inc,2005.1221-1224.
  • 6Dao-qing Dai,Pong C.Yuenv.Face recognition by regularized discriminant analysis[J].Systems,Man,and Cybernetics part B-cybernetics,2007,37(4):1080-1085.
  • 7Ye Jieping,Li Qi.A two-stage linear discriminant analysis via QR-decomposition[J].Pattern Analysis and Machine Intelligence,2005,27(6):929-941.
  • 8Li-fen Chen,Hong-yuan Mark Liao,Ming-Tat Ko.A new LDA-based face recognition system which can solve the small sample size problem[J].Pattern Recognition,2000,33(10) :1713-1726.
  • 9Liu Jun,Chen Songcan.Discriminant common vecotors versus neighbourhood components analysis and laplacianfaces:A comparative study in small sample size problem[J].Image and Vision Computing,2006,24(3):249-262.
  • 10Wang Xiaogang,Tang Xiaoou.Random sampling for subspace face recognition[J].International Journal of Computer Vision,2006,70(1):94-104.

共引文献1

同被引文献14

  • 1Atkins W.Industry squares up to multiple opportunities[J].Biometric Technology Today,2007,15(1):8-11.
  • 2Ma Hui,Hu Fengsong.The study of human face recognition based curvelet transform and 2DPCA[J].ICISE,2010,12(4/6):5512-5515.
  • 3杨利平,叶洪伟.人脸识别的相对梯度方向边缘幅值模式[J].中国科技论文在线精品论文,2014,7(4).
  • 4Ramasubramanian D,Venkatesh Y V.Encoding and recognition of faces based on the human visual model and DCT[J].Pattern Recognition,2001,34(12):2447-2458.
  • 5Tseng C C.Eigenvalues and eigenvectors of generalized DFT,generalized DHT,DCT-IV and DST-IV matrices[J].IEEE Trans on SP,2002,SP-50(4):866-877.
  • 6Chen Weilong,Er M J,Wu Shiqian.PCA and LDA in DCT domain[J].Pattern Recognition Letters,2005,26:2474-2482.
  • 7Vishwakarma P,Pandey S,Gupta M N.An illumination invariant accurate face recognition with down scaling of DCT coefficients[J].Journal of Computing and Information Technology,2010,18(1):53-67.
  • 8Celik T,Tjahjadi T.Automatic image equalization and contrast enhancement using Gaussian mixture modeling[J].IEEE Transactions on Image Processing,2012,21(1):145-156.
  • 9Chen Weilong,Meng Joo Er,Wu Shiqian.Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain[J].IEEE Transactions on Systems,Man,and Cybernetics,PartB:Cybernetics,2006,36(2):458-466.
  • 10苏煜,山世光,陈熙霖,高文.基于全局和局部特征集成的人脸识别[J].软件学报,2010,21(8):1849-1862. 被引量：117

引证文献1

吉林大学学报（理学版）
2013年 第3期

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