Individual kernel tensor-subspaces for robust face recognition: A computationally efficient tensor framework without requiring mode factorization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 37, Issue 5, 2007, Pages 1156-1166

  Park, Sung Won ^a   Savvides, Marios ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Face recognition; Higher order singular value decomposition (HOSVD); Individual principal component analysis (PCA); Individual modeling approach; Multilinear analysis; Nonlinear kernel mappings; TensorFaces

Indexed keywords

FACTORIZATION; MATHEMATICAL MODELS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; SINGULAR VALUE DECOMPOSITION; TENSORS;

HIGHER ORDER SINGULAR VALUE DECOMPOSITION (HOSVD); HIGHER ORDER TENSORS; INDIVIDUAL PRINCIPAL COMPONENT ANALYSIS; MULTILINEAR ANALYSIS; NONLINEAR KERNEL MAPPINGS; ROBUST FACE RECOGNITION;

FACE RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BIOMETRY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; FACE; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; COMPUTER SIMULATION; FACE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 35148895418     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2007.904575     Document Type: Article

References (25)

1. M. A. O. Vasilescu and D. Terzopoulos, "Multilinear analysis of image ensembles : TensorFaces," in Proc. Eur. Conf. Comput. Vis., May 2002, vol. 1, pp. 447-460.
2. M. A. O. Vasilescu and D. Terzopoulos, "Multilinear image analysis for facial recognition," in Proc. IEEE Conf. Pattern Recog., Aug. 2002, vol. 2, pp. 511-514.
3. M. A. Vasilescu and D. Terzopoulos, "Multilinear independent components analysis," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2005, vol. 1, pp. 547-553.
4. M. A. O. Vasilescu and D. Terzopoulos, "Multilinear subspace analysis of image ensembles," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. Jun. 2003, vol. 2, pp. 93-99.
5. L. D. Lathauwer, B. D. Moor, and J. Vandewalle, "A multilinear singular value decomposition," SIAM J. Matrix Anal. Appl., vol. 21, no. 4, pp. 1254-1278, Mar.-May 2000.
6. L. D. Lathauwer, B. D. Moor, and J. Vandewalle, "On the best rank-1 and rank-(R1, R2,...,RN) approximation of higher-order tensors," SIAM J. Matrix Anal. Appl., vol. 21, no. 4, pp. 1324-1342, Mar.-May 2000.
7. M. A. Turk and A. P. Pentland, "Eigenfaces for recognition," J. Cogn. Neurosci., vol. 3, no. 1, pp. 71-86, 1991.
8. J. B. Tenenbaum and W. T. Freeman, "Separating style and content with bilinear models," Neural Comput., vol. 12, no. 6, pp. 1246-1283, Jun. 2000.
9. D. Lin, Y. Xu, X. Tang, and S. Yan, "Tensor-based factor decomposition for relighting," in Proc. IEEE Int. Conf. Image Process., Sep. 2005, vol. 2, pp. 386-389.
10. Y. Ki, Y. Du, and X. Lin, "Kernel-based multifactor analysis for image synthesis and recognition," in Proc. IEEE Int. Conf. Comput. Vis. Oct. 2005, vol. 1, pp. 114-119.
11. J. B. Tenenbaum and W. T. Freeman, "Learning style translation for the lines of a drawing," ACM Trans. Graph., vol. 22, no. 1, pp. 33-46, Jan. 2003.
12. H. Wang and N. Ahuja, "Facial expression decomposition," in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2003, vol. 2, pp. 958-965.
13. B. Abboud and F. Davoine, "Bilinear factorisation for facial expression analysis and synthesis," Proc. Inst. Electr. Eng., Vision, Image Signal Process., vol. 152, no. 3, pp. 327-333, Jun. 2005.
14. J. Lee, B. Moghaddam, H. Pfister, and R. Machiraju, "A bilinear illumination model for robust face recognition," in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2005, vol. 2, pp. 1177-1184.
15. S. W. Park and M. Savvides, "Estimating mixing factors simultaneously in multilinear tensor decomposition for robust face recognition and synthesis," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. Workshop Jun. 2006, p. 49.
16. S. Saitoh, Theory of Reproducing Kernels and Its Applications. Harlow, U.K.: Longman Sci. and Tech., 1988.
17. B. Schökopf, A. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, 1998.
18. Z. Zhang and Y. Huang, "A projection method for least squares problems with a quadratic equality constraint," SIAM J. Matrix Anal. Appl. vol. 25, no. 1, pp. 188-212, 2003.
19. T. G. Kolda, "Orthogonal tensor decomposition," SIAM J. Matrix Anal. Appl., vol. 23, no. 1, pp. 243-255, 2003.
20. A. Graham, Kronecker Products and Matrix Calculus With Applications New York: Halsted, 1981.
21. I. Newton, Methodus Fluxionum Et Serierum Infinitarum. 1664-1671.
22. G. H. Golub and U. V. Matt, "Quadratically constrained least squares and quadratic problems, Numer. Math., vol. 59, no. 1, pp. 561-580, Dec. 1991.
23. X. Liu, T. Chen, and B. V. K. Vijaya Kumar, "Face authentication for multiple subjects using eigenflow," Pattern Recognit., vol. 36, no. 2, pp. 313-328, Feb. 2003.
24. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, 2000.
25. T. Sim, S. Baker, and M. Bsat, "The CMU pose, illumination, and expression database," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1615-1618, Dec. 2003.