Symbolic Kernel Fisher Discriminant Method With a New RBF Kernel Function for Face Recognition

• Autorzy: Hiremath P. S. , Prabhakar C. J.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we present a new radial basis kernel function (RBF) in symbolic kernel Fisher discriminant analysis (symbolic KFD) to extract nonlinear interval type features for face recognition. The kernel-based methods form a powerful paradigm, they are not favorable to deal with the challenge of large datasets of faces. We propose to scale up training task based on the interval data concept. Our investigation aims at extending KFD to interval data using new RBF kernel function. We adapt symbolic KFD to extract interval type nonlinear discriminating features, which are robust enough to varying facial expression, viewpoint and illumination. In the classification phase, we employ the minimum distance classifier with the squared Euclidean distance measure. The new algorithm has been successfully tested using four databases, namely, the ORL face database, the Yale face database, the Yale face database B and the FERET face database. The experimental results show that the symbolic KFD with the new RBF kernel function yields improved performance.

Słowa kluczowe

EN

face recognition; symbolic data analysis; kernel fisher discriminant analysis; interval type features; RBF kernel function

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2009
• Tom: Vol. 18, No. 4
• Strony: 383--404
• Opis fizyczny: Bibliogr. 38 poz., il., tab., wykr.

Twórcy

autor

Hiremath P. S.

autor

Prabhakar C. J.

• Department of Studies Computer Science, Gulbarga University, Gulbarga, Karnataka, India

Bibliografia

• [1] Turk, Pentland: Eigenfaces for Recognation, 3, J Cognitive Neuro Science, 71-86. 1991.
• [2] Diday: An Introduction to symbolic data analysis. Tutorial at IV Conf. IFCS. 1993.
• [3] Samaria, Harter: Parameterization of a stochastic model for human face identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision. 1993.
• [4] Belhumeur P., Hespanha J., Kriegman D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transaction on PAMI. 19(7), 711-720. 1997.
• [5] Mika, Ratsch, Scholkopf, Muller: Fisher Discriminant Analysis with kernels. Proc. EEE Int Workshop Neural Networks for Signal Processing , 41-48. 1999.
• [6] Zhao, Chellappa, Phillips: Subspace linear discriminant analysis for face recognition. Technical Report, CS-TR4009, University of Maryland.1999.
• [7] Bock, H.H., Diday, E.(Eds.):Analysis of Symbolic Data. Springer Verlag. 2000.
• [8] Chen, Belhumeur, Jacobs: In search of illumination invariants. In Proc. IEEE CVPR 2000, 254-261, 2000.
• [9] Feng G.C., Yuen P.C., and Dai D.Q.: Human face recognition using PCA on wavelet subband. J. Electronic Imaging, 9(2), 226-233. 2000.
• [10] Kernel Machines website: http://www.kernel-machines.org. 2000.
• [11] Liu, Wechsler: Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. On image processing, 132-137. 2000.
• [12] Yang M.H., Ahuja N. and Kriegman D.: Face Recognition Using Kernel Eigenfaces. Proc. IEEE Int'l Conf. Image Processing. 2000.
• [13] MA. Basri, Jacobs: Lambertian reflectance and linear subspaces. In Proc. IEEE ICCV 2001, 383-390, 2001.
• [14] Georghiades A.S., Belhumeur P.N., and Kriegman D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 643-660. 2001.
• [15] Hafed Z.M. and Levine M.D.: Face recognition using the discrete cosine trasnforms. Int. J. Comput. Vis., 43(3), 167-188. 2001.
• [16] Muller, Mika, Ratsch, Tsuda, Scholkopf: An Introduction to kernel based Learning Algorithms. IEEE Trans. Neural Networks, 12, 255-261. 2001.
• [17] Yu, Yang : A Direct LDA algorithm for high dimensional data with application to face recognition. Pattern Recognition, 34(7), 2067-2070. 2001.
• [18] Shan S., Gao W., and Zhao D.: Face identification from a single example image based on face-specfic subspace (FSS). IEEE International Conference on Acoustic, Speech and Signal Processing, ICASSP 2002, 150-155. 2002.
• [19] Yang M.H.: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition using Kernel Methods. Proc. Fifth IEEE Int'l Conf. Automatic Face and Gesture Recognition, 215-220. 2002.
• [20] Jianke M., V. Mang I., Un M.P.: Face Recognition Using 2D DCT with PCA. The 4th Chinese Conference on Biometric Recognition (Sinobiometrics03), Beijing, China, 7-8. 2003.
• [21] Lu J., Plataniotis K.N., and Venetsanopoulus A.N.: Face recognition using LDA-based algorithmn. IEEE Trans. Neural Network, 14(1), 195-200. 2003.
• [22] Lu, Plataniotis, Venetsanopoulos: Face Recognition using LDA based algorithms. IEEE Trans Neural Networks, 1(1),195-200. 2003.
• [23] Zhao, Chellappa, Phillips, Rosenfeld: Face Recognition: A literature survey. ACM Comput. Surveys 35(4), 399-458. 2003.
• [24] Phillips P.J.: The Facial Recognition Technology (FERET) Database. http ://www.itl.nist.gov/iad/humanid/feret/feretmaster.html. 2004.
• [25] Ye, Li: LDA/QR An efficient and effective dimension reduction algorithm and its theoretical foundation. Pattern Recognition, 27(9), 1209-1230. 2004.
• [26] Chen W., Er M.J., and Wu S.: PCA and LDA in DCT domain. ELSEVIER Pattern Recognition Letter, 26, 2474-2482. 2005.
• [27] Hiremath. P.S, Prabhakar. C.J.: Face Recognition Technique using Symbolic PCA Method. Proc. Int. Conf. on Pattern Recognition and Machine Intelligence (PreMI'05), Kolkata, Springer LNCS, 266-271. 2005.
• [28] Jing X.Y., H S Wong. D Zhang and Y. Y Tang: An uncorrelated Fisherface approach. Neuro Computing Letter, Elsevier. 2005.
• [29] Lee K., Jeffrey Ho, David Kriegman: Acquiring Linear Subspaces for Face Recognition under Variable Lighting. IEEE Trans. Pattern Anal. Machine Intell. 27(5), 1-15. 2005.
• [30] Nowosielski A.: Face Recognition Using DCT and LDA. Advances in Soft Computing, Computer Recognition Systems. Springer, 799-806. 2005.
• [31] Yang J., Frangi A.F., Yang J., Zhang D. and Jin Z.: KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Trans. On PAMI, 27(2), 230-243. 2005.
• [32] Dai D.Q., Yuen P.C.: Wavelet based discriminant analysis for face recognition. ELSEVIER Applied Mathematics and Computation, 175, 307-318. 2006.
• [33] Hiremath. P.S, Prabhakar. C.J.: Acquiring Non Linear Subspace for Face Recognition using Symbolic Kernel PCA Method. JSDA Electronic Journal of Symbolic Data Analysis, 4(1), 1723-5081. 2006.
• [34] Hiremath. P.S, Prabhakar. C.J.: Face Recognition Technique Using Symbolic Linear Discriminant Analysis Method. Proceedings of ICVGIP 2006, P. Kalra and S. Peleg (Eds.): Springer Verlag-LNCS 4338, pp. 641-649.2006.
• [35] Forczmanski P., Kukharev G.: Comparative analysis of simple facial features extractors. Journal of Real Time Image Processing, 1(4), 239-255. 2007.
• [36] Hiremath P.S, Prabhakar. C.J.: Face Recognition Technique Using Symbolic KDA in the framework of Symbolic Data Analysis. Proceedings of ICAPR 2006, World Scientific Publisher, 56-61. 2007.
• [37] Jiang X., Mandal B and Alex Kot: Complete discriminant evaluation and feature extraction in kernel space for face recognition. Machine Vision and Applications, Springer LNCS, 20, 35-46.2007.
• [38] Hiremath P.S., Prabhakar C.J.: Symbolic Factorial Discriminant Analysis for llumination Invariant Face Recognition. International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publisher, 22(3), 371-387. 2008.