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鉴别投影嵌入及其在人脸识别中的应用

严严 章毓晋

严严, 章毓晋. 鉴别投影嵌入及其在人脸识别中的应用[J]. 电子与信息学报, 2008, 30(12): 2902-2905. doi: 10.3724/SP.J.1146.2007.00864
引用本文: 严严, 章毓晋. 鉴别投影嵌入及其在人脸识别中的应用[J]. 电子与信息学报, 2008, 30(12): 2902-2905. doi: 10.3724/SP.J.1146.2007.00864
Yan Yan, Zhang Yu-Jin. Discriminant Projection Embedding with Its Application to Face Recognition[J]. Journal of Electronics and Information Technology, 2008, 30(12): 2902-2905. doi: 10.3724/SP.J.1146.2007.00864
Citation: Yan Yan, Zhang Yu-Jin. Discriminant Projection Embedding with Its Application to Face Recognition[J]. Journal of Electronics and Information Technology, 2008, 30(12): 2902-2905. doi: 10.3724/SP.J.1146.2007.00864

鉴别投影嵌入及其在人脸识别中的应用

doi: 10.3724/SP.J.1146.2007.00864
基金项目: 

国家自然科学基金(60573148)资助课题

Discriminant Projection Embedding with Its Application to Face Recognition

  • 摘要: 该文提出了一种新的监督线性降维方法,称为鉴别投影嵌入(Discriminant Projection Embedding, DPE)。和常用的线性鉴别分析相比,鉴别投影嵌入可以更好地保留类内的局部几何位置信息和提取类间的鉴别结构信息。在人脸识别公用数据库上进行了一系列的实验,实验结果表明了该文方法的可行性和有效性。
  • [1] Yan S C, Xu D, Zhang B, and Zhang H J. Graph embeddingand extensions: A general framework for dimensionalityreduction[J].IEEE Trans. on Pattern Analysis and MachineIntelligence.2007, 29(1):40-51 [2] Moghaddam B. Principal manifolds and probabilisticsubspace for visual recognition[J].IEEE Trans. on PatternAnalysis and Machine Intelligence.2002, 24(6):780-788 [3] Zhao W, Chellappa R, Phillips P J, and Rosenfeld A. Facerecognition: A literature survey[J].ACM Computing Surveys.2003, 35(4):399-459 [4] Zhao W, Chellappa R, and Krishnaswamy A. Discriminantanalysis of principal components for face recognition.Proceedings of IEEE International Conference on AutomaticFace and Gesture Recognition, Nara, Japan, 1998: 336-341. [5] Belhumeur P N, Hepanha J P, and Kriegman D J. Eigenfacesvs Fisherfaces: recognition using class specific linearprojection[J].IEEE Trans. on Pattern Analysis and MachineIntelligence.1997, 19(7):711-720 [6] Bressan M and Vitria J. Nonparametric discriminant analysisand nearest neighbor classification[J].Pattern RecognitionLetters.2003, 24(15):2743-2749 [7] Chen H T, Chang H W, and Liu T L. Local discriminantembedding and its variants. Proceedings of IEEEInternational Conference on Computer Vision and PatternRecognition, San Diego, USA, 2005: 846-853. [8] Chen L, Liao H, Ko M, Lin J, and Yu G. A new LDA-basedface recognition system which can solve the small sample sizeproblem[J].Pattern Recognition.2000, 33(10):1713-1726 [9] Yu H and Yang J. A direct LDA algorithm forhigh-dimensional data with application to face recognition[J].Pattern Recognition.2001, 34(10):2067-2070 [10] Wang X G and Tang X O. A unified framework for facerecognition[J].IEEE Trans. on Pattern Analysis and MachineIntelligence.2004, 26(9):1222-1228 [11] Roweis S and Saul L K. Nonlinear dimensionality reductionby locally linear embedding[J].Science.2000, 290(5500):2323-2326 [12] He X, Cai D, Yan S, and Zhang H J. Neighborhoodpreserving embedding. Proceedings of IEEE InternationalConference on Computer Vision, Beijing, China, 2005:1208-1213. [13] Fukunaga K. Introduction to Statistical Pattern Recognition.New York: Academic Press, 1990: 466-479. [14] You Q, Zhen N, Du S, and Wu Y. Neighborhood discriminantanalysis for face recognition. Pattern Recognition Letters,2007, 40(8): 2283-2291.
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  • 被引次数: 0
出版历程
  • 收稿日期:  2007-06-04
  • 修回日期:  2007-09-13
  • 刊出日期:  2008-12-19

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