Fusing gabor and LBP feature sets for kernel-based face recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4778 LNCS, Issue , 2007, Pages 235-249

  Tan, Xiaoyang ^a   Triggs, Bill ^a  

^a UNIV GRENOBLE ALPES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION;

GABOR FEATURES; HETEROGENEOUS FEATURE SETS; KERNEL DISCRIMINATIVE COMMON VECTOR; LOCAL BINARY PATTERNS (LBP);

FACE RECOGNITION;

EID: 38149044888     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75690-3_18     Document Type: Conference Paper

References (32)

1. Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469-481. Springer, Heidelberg (2004)
2. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE TPAMI 28(12) (2006)
3. Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Fusion of face and speech data for person identity verification. IDIAP-RR 03, IDIAP (1999)
4. Cevikalp, H., Neamtu, M., Wilkes, M.: Discriminative common vector method with kernels. IEEE TNN 17(6), 1550-1565 (2006)
5. Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE TPAMI 27(1), 4-13 (2005)
6. Chang, K., Bowyer, K., Flynn, P., Chen, X.: Multi-biometrics using facial appearance, shape and temperature. In: Proc. AFGR 2004, pp. 43-48 (2004)
7. Chen, L.-F., Liao, H.-Y.M., Ko, M.-T., Lin, J.-C., Yu, G.-J.: A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(10), 1713-1726 (2000)
8. Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educational Psychology 24, 417-441 (1933)
9. Huang, Y.S., Suen, C.Y.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE TPAMI 17(1), 90-94 (1995)
10. Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38(12), 2270-2285 (2005)
11. Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE TPAMI 20(3), 226-239 (1998)
12. Lades, M., Vorbruggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300-311 (1993)
13. Lai, P.L., Fyfe, C.: Kernel and nonlinear canonical correlation analysis. IJCNN 04, 4614 (2000)
14. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556-566 obtainable2 (2000)
15. Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE TPAMI 28(5), 725-737 (2006)
16. Lu, C., Wechsler, H.: A shape- and texture-based enhanced fisher classifier for face recognition. IEEE TIP 10(4), 598-608 (2001)
17. Lu, X., Wang, Y., Jain, A.: Combining classifiers for face recognition. In: ICME 2003. Multimedia and Expo. 2003 (2003)
18. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29 (1996)
19. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invarianat texture classification with local binary patterns. IEEE TPAMI 24(7), 971-987 (2002)
20. Comon, P.: Independent component analysis-a new concept? Signal Processing 43, 287-314 (1994)
21. Phillips, P.J., Flynn, P.J., Scruggs, W.T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.J.: Overview of the face recognition grand challenge. In: Proc. CVPR 2005, San Diego, CA, pp. 947-954 (2005)
22. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE TPAM 22(10), 1090-1104 (2000)
23. Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recogn. Lett. 24(13), 2115-2125 (2003)
24. Fisher, R.R.: The use of multiple measurements in taxonomic problems. Ann. Eugen 7, 179-188 (1936)
25. Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
26. Swets, D.L., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE TPAMI 18(8), 831-836 (1996)
27. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., et al. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168182. Springer, Heidelberg (2007)
28. Wiskott, L., Fellous, J.-M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE TPAMI 19(7), 775-779 (1997)
29. Yang, J., Yang, J.-Y.: Generalized k-l transform based combined feature extraction. Pattern Recognition 35(1), 295-297 (2002)
30. Zhang, W., Shan, S., Gao, W., Zhang, H.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for face representation and recognition. In: Proc. ICCV 2005, Beijing, China, pp. 786-791 (2005)
31. Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: FG 1998, Washington, DC, USA, p. 336 (1998)
32. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Survey 34(4), 399-485 (2003)