Globality-locality preserving projections for biometric data dimensionality reduction

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2014, Pages 15-20

  Huang, Sheng ^a   Elgammal, Ahmed ^b   Huangfu, Luwen ^c   Yang, Dan ^a   Zhang, Xiaohong ^a  

^a CHONGQING UNIVERSITY   (China)

^b RUTGERS UNIVERSITY   (United States)

^c HTC Beijing Advanced Technology and Research Center   (China)

Author keywords

Dimensionality Reduction; Face Recognition; Gait Recognition; Graph Laplacian; Subspace Learning

Indexed keywords

BIOMETRICS; COMPUTER VISION; DIMENSIONALITY REDUCTION; FACE RECOGNITION; GAIT ANALYSIS; GRAPH ALGORITHMS; LAPLACE TRANSFORMS;

BIOMETRIC RECOGNITION; DIMENSIONALITY REDUCTION ALGORITHMS; GAIT RECOGNITION; GRAPH LAPLACIAN; LOCALITY PRESERVING PROJECTIONS; MANIFOLD STRUCTURES; STATE-OF-THE-ART ALGORITHMS; SUBSPACE LEARNING;

DATA REDUCTION;

EID: 84908504635     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2014.8     Document Type: Conference Paper

References (32)

1. T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. TMMI, 28(12):2037-2041, 2006.
2. P. N. Belhumeur, P Hespanha, and D. J. Kriegman. Eigen-faces vs. fisherfaces: Recognition using class specific linear projection. TPAMI, pages 711-720, 1997.
3. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, vol ume 14, pages 585-591, 2001.
4. D. Cai, X. He, J. Han, and H. Zhang. Orthogonal laplacian-faces for face recognition. Tff, 15(11):3608-3614, 2006.
5. D. Chen, X. Cao, F. Wen, and J. Sun. Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In CVPR, pages 3025-3032, 2013.
6. S. Chen, H. Zhao, M. Kong, and B. Luo. 2d-lpp: A two-dimensional extension of locality preserving projections. I-7CW, 70(4-6):912-921, 2007.
7. F. R. K. Chung. Spectral graph theory, cbms regional conference series in mathematics, 1996.
8. R. Gross andJ. Shi. The cmu motionofbody(mobo) database. Technical Report CMU-RI-TR-01-18, 2001.
9. J. Han and B. Bhanu. Individual recognition using gait energy image. T/AMI, 28(2):316-322, 2006.
10. X. He, D. Cai, S. Yan, and H. Zhang. Neighborhood preserving embedding. In ICCV, volume 2, pages 1208-1213, 2005.
11. X. He and P. Niyogi. Locality preserving projections. In NIPS, 2003.
12. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Jiang Zhang. Face recognition using laplacianfaces. TfAMI, 27:328-340, 2005.
13. S. Huang, A. Elgammal, and D. Yang. Learning speed invariant gait template via thin plate spline kernel manifold fitting. In BMVC, 2013.
14. W. Kusakunniran, Q. Wu, J. Zhang, and H. Li. Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model. TMSCB, pages 1654-1668, 2012.
15. D. D. Lee and H. S. Seung. Learning the parts of objects by nonnegative matrix factorization. Ataure, 401:788-791, 1999.
16. M. Li and B. Yuan. 2d-lda: A statistical linear discriminant analysis for image matrix. PRL, 26(5):527-532, 2005.
17. J. Lu and Y.-P. Tan. Regularized locality preserving projections and its extensions for face recognition. TSMCB, 40(3):958-963, 2010.
18. A. Martinez and R. Benavente. The ar face database. Technical Report 24, Computer Vision Center, Bellatera, Jun 1998.
19. I. Naseem, R. Togneri, and M. Bennamoun. Linear regression for face recognition. T/AMI, 32(11):2106-2112, 2010.
20. P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss. The FER-ET database and evaluation procedure for face recognition algorithms. IVC, 16(5):295-306, 1998.
21. S. T. Roweis andL. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323-2326, 2000.
22. F. S. Samaria, F. S. Samaria, A. Harter, and O. Addenbrooke. Parameterisation of a stochastic model for human face identification, 1994.
23. D. Tao, X. Li, X. Wu, and S. Maybank. General tensor discriminant analysis and gabor features for gait recognition. TAMI, 29(10):1700-1715, 2007.
24. A. Tsuji, Y. Makihara, and Y. Yagi. Silhouette transformation based on walking speed for gait identification. In CVPR, pages 717-722, 2010.
25. M. Turk and A. Pentland. Eigenfaces for recognition. J. Cognitive Neuroscience, 3(1):71-86, Jan. 1991.
26. C. Wang, J. Zhang, L. Wang, J. Pu, and X. Yuan. Human identification using temporal information preserving gait template. TAMI, 34:2164-2176, 2012.
27. L. Wolf, T. Hassner, and Y. Taigman. Similarity scores based on background samples. In ACCV, pages 88-97, 2009.
28. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. TPAMI, 31, 2009.
29. J. Yang, D. Zhang, A. Frangi, and J.-Y. Yang. Two-dimensional pca: A new approach to appearance-based face representation and recognition. T/AMI, 26(1):131-137, 2004.
30. M. Yang, D. Zhang, and S. Wang. Relaxed collaborative representation for pattern classification. In CWR, pages 2224-2231, 2012.
31. W. Yu, X. Teng, and C. Liu. Face recognition using discriminant locality preserving projections. IVC, 24(3):239-248, 2006.
32. T. Zhang, B. Fang, Y. Y. Tang, Z. Shang, and B. Xu. Generalized discriminant analysis: A matrix exponential approach. TSMCB, 40(1):186-197, 2010.