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Median Fisher Discriminator: a robust feature
extraction method with applications to biometrics |
| YANG Jian1, YANG Jingyu1, ZHANG David2 |
| 1.School of Computer Science and Technology, Nanjing University of Science and Technology; 2.Biometric Research Centre, Department of Computing, Hong Kong Polytechnic University |
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Abstract In existing Linear Discriminant Analysis (LDA) models, the class population mean is always estimated by the class sample average. In small sample size problems, such as face and palm recognition, however, the class sample average does not suffice to provide an accurate estimate of the class population mean based on a few of the given samples, particularly when there are outliers in the training set. To overcome this weakness, the class median vector is used to estimate the class population mean in LDA modeling. The class median vector has two advantages over the class sample average: (1) the class median (image) vector preserves useful details in the sample images, and (2) the class median vector is robust to outliers that exist in the training sample set. In addition, a weighting mechanism is adopted to refine the characterization of the within-class scatter so as to further improve the robustness of the proposed model. The proposed Median Fisher Discriminator (MFD) method was evaluated using the Yale and the AR face image databases and the PolyU(Polytechnic University) palmprint database. The experimental results demonstrated the robustness and effectiveness of the proposed method.
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Issue Date: 05 September 2008
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| 1 |
Webb A . StatisticalPattern Recognition. London: Hodder Arnold, 1999
|
| 2 |
Turk M, Pentland A . Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71–86.
doi:10.1162/jocn.1991.3.1.71
|
| 3 |
Lu G, Zhang D, Wang K . Palmprint recognition using eigenpalms features. Pattern Recognition Letters, 2003, 24(9–10): 1463–1467.
doi:10.1016/S0167‐8655(02)00386‐0
|
| 4 |
Liu K, Cheng Y -Q, Yang J -Y, et al.. An efficient algorithm for Foley-Sammon optimalset of discriminant vectors by algebraic method. International Journal of Pattern Recognition and Artificial Intelligence, 1992, 6(5): 817–829.
doi:10.1142/S0218001492000412
|
| 5 |
Swets D L, Weng J . Using discriminant eigenfeaturesfor image retrieval. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1996, 18(8): 831–836.
doi:10.1109/34.531802
|
| 6 |
Belhumeur P N, Hespanha J P, Kriengman D J . Eigenfaces vs. fisherfaces: recognition using class specificlinear projection. IEEE Transactions onPattern Analysis and Machine Intelligence, 1997, 19(7): 711–720.
doi:10.1109/34.598228
|
| 7 |
Chen L F, Liao H -Y M, Lin J C, et al.. A new LDA-based face recognition system whichcan solve the small sample size problem. Pattern Recognition, 2000, 33(10): 1713–1726.
doi:10.1016/S0031‐3203(99)00139‐9
|
| 8 |
Jin Z, Yang J Y, Hu Z S, et al.. Face recognition based on uncorrelated discriminanttransformation. Pattern Recognition, 2001, 34(7): 1405–1416.
doi:10.1016/S0031‐3203(00)00084‐4
|
| 9 |
Yu H, Yang J . A direct LDA algorithm forhigh-dimensional data-with application to face recognition. Pattern Recognition, 2001, 34(10): 2067–2070.
doi:10.1016/S0031‐3203(00)00162‐X
|
| 10 |
Yang J, Yang J Y, Why can LDA be performedin PCA transformed space? Pattern Recognition, 2003, 36(2): 563–566.
doi:10.1016/S0031‐3203(02)00048‐1
|
| 11 |
Liu C J, Wechsler H . Robust coding schemes forindexing and retrieval from large face databases. IEEE Transactions on Image Processing, 2000, 9(1): 132–137.
doi:10.1109/83.817604
|
| 12 |
Kim T -K, Kittler J . Locally linear discriminantanalysis technique for multi-modally distributed classes for facerecognition with a single model image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 318–327.
doi:10.1109/TPAMI.2005.58
|
| 13 |
Wu X, Zhang D, Wang K . Fisherpalms based palmprint recognition. Pattern Recognition Letters, 2003, 24(15): 2829–2838.
doi:10.1016/S0167‐8655(03)00141‐7
|
| 14 |
Yang M H . Kernel eigenfaces vs. kernel fisherfaces: face recognition usingkernel methods. In: : Proceed-ings of theFifth IEEE International Conference on Automatic Face and GestureRecognition. Washington D. C., 2002 : 215–220
|
| 15 |
Lu J, Plataniotis K N, Venetsanopoulos A N . Face recognition using kernel directdiscriminant analysis algorithms. IEEETransactions on Neural Networks, 2003, 14(1): 117–126.
doi:10.1109/TNN.2002.806629
|
| 16 |
Yang J, Frangi A, Yang J Y, et al.. KPCA plus LDA: a complete kernel Fisher discriminantframework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2): 230–244.
doi:10.1109/TPAMI.2005.33
|
| 17 |
Loog M, Duin R P W, Haeb-Umbach R . Multiclass linear dimension reduction by weighted pairwiseFisher criteria. IEEE Transactions on PatternAnalysis and Machine Intelligence, 2001, 23(7): 762–766.
doi:10.1109/34.935849
|
| 18 |
Koren Y, Carmel L . Robust linear dimensionalityreduction, IEEE Transactions on Visualizationand Computer Graphics, 2004, 10(4): 459–470.
doi:10.1109/TVCG.2004.17
|
| 19 |
He X, Yan S, Hu Y, et al.. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2005, 27(3): 328–340.
doi:10.1109/TPAMI.2005.55
|
| 20 |
Kwak K-C, Pedrycz W . Face recognition using afuzzy fisherface classifier. Pattern Recognition, 2005, 38(10): 1717–1732.
doi:10.1016/j.patcog.2005.01.018
|
| 21 |
Fidler S, Skocaj D, Leonardis A . Combining reconstructive and discriminative subspacemethods for robust classification and regression by subsampling. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2005, 27(3): 328–340.
doi:10.1109/TPAMI.2005.55
|
| 22 |
Chen S C, Liu J, Zhou Z -H, Making FLDA applicable to face recognition with one sampleper person. Pattern Recognition, 2004, 37(7): 1553–1555.
doi:10.1016/j.patcog.2003.12.010
|
| 23 |
Huang J, Yuen P C, Chen W S, et al.. Component-based LDA method for face recognitionwith one training sample. AMFG (Analysisand Modeling of Faces and Gestures), 2003, 120–126
|
| 24 |
Hawkins D M, McLachlan G J . High-Breakdown linear discriminantanalysis. Journal of the American StatisticalAssociation, 1997, 92: 136–143.
doi:10.2307/2291457
|
| 25 |
He X, Fung W K . High breakdown estimationfor multiple populations with applications to discriminant analysis. Journal of Multivariate Analysis, 2000, 72(2): 151–162.
doi:10.1006/jmva.1999.1857
|
| 26 |
Hubert M, Driessen K V . Fast and robust discriminantanalysis. Computational Statistics and Data Analysis, 2003, 45: 301–320.
doi:10.1016/S0167‐9473(02)00299‐2
|
| 27 |
Croux C, Dehon C . Robust linear discriminantanalysis using S-estimators. Canadian Journalof Statistics, 2001, 29: 473–493.
doi:10.2307/3316042
|
| 28 |
Croux C, Dehon C, Rousseeuw P J, et al.. Robust estimation of the conditional medianfunction at elliptical models. Statistics &Probability Letters, 2001, 51: 361–368.
doi:10.1016/S0167‐7152(00)00176‐0
|
| 29 |
Rousseeuw P S, Driessen K V . A fast algorithm for theminimum covariance determinant estimator. Technometrics, 1999 (41): 212–223
|
| 30 |
Grimmett G R, Stirzaker D R . Probability and Random Processes. 2nd ed . Oxford: Clarendon Press, 1992
|
| 31 |
Wikipedia. . Thefree encyclopedia. http://en.wikipedia.org/wiki/Median
|
| 32 |
Marion A . AnIntroduction to Image Processing. London:Chapman and Hall, 1991
|
| 33 |
Yale face database. http://cvc.yale.edu/projects/yalefaces/yalefaces.html
|
| 34 |
Martinez A M, Benavente R . The AR face database. http://rvl1.ecn.purdue.edu/˜aleix/aleix_face_DB.html
|
| 35 |
Martinez A M, Benavente R . The AR face database. Computer Vision Center Technical Report #24, 1998
|
| 36 |
Zhang D D . Palmprint Authentication. Berlin: Springer, 2004
|
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