Feature space-based human face image representation and recognition

Optical Engineering

Volumn 51, Issue 1, 2012, Pages

  Xu, Yong ^a   Fan, Zizhu ^a   Zhu, Qi ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

classification; computer vision; image representation

Indexed keywords

FACE IMAGES; FACE RECOGNITION METHODS; FEATURE SPACE; HUMAN FACE IMAGE; IMAGE REPRESENTATION; KERNEL METHODS; LINEAR COMBINATIONS; SPACE-BASED; TEST SAMPLES; TRAINING SAMPLE; IMAGE REPRESENTATIONS;

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; FEATURE EXTRACTION; ENGINEERING; MOLECULAR PHYSICS;

FACE RECOGNITION;

EID: 84863057511     PISSN: 00913286     EISSN: 15602303     Source Type: Journal    
DOI: 10.1117/1.OE.51.1.017205     Document Type: Article

References (39)

1. Littlewort, G. C. et al., Towards social robots: automatic evaluation of human-robot interaction by face detection and expression classification., in Advances in Neural Information Processing Systems, S. Thrun, L. Saul, and, B. Schoelkopf, Eds., vol. 16, pp. 1563-1570, MIT Press, Cambridge, Mass (2004).
2. Ekenel, H. K. et al., Face recognition for smart interactions., in Proc. of the 2007 IEEE International Conference on Multimedia and Expo, IEEE Computer Society, Beijing, China, pp. 1007-1010 (2007).
3. Ekenel, H. K., M. Fischer and R. Stiefelhagen, Face recognition in smart rooms., in the MLMI 2007 Proc. of the 4th International Conference on Machine Learning for Multimodal Interaction, A. Popescu-Belis, H. Bourlard, and, S. Renals, Eds., Springer-Verlag Berlin, Heidelberg, Germany, pp. 120-131 (2007). (Pubitemid 351337520)
4. Cruz, C., L. E. Sucar and E. F. Morales, Real-time face recognition for human-robot interaction., in Proc. of the 8th. IEEE International Conference on Automatic Face and Gesture Recognition, IEEE Computer Society, Amsterdam, The Netherlands, pp. 1-6 (2008).
5. Fukui, K. and O. Yamaguchi, Face recognition using multiviewpoint patterns for robot vision., Robot. Res. 192-201 (2003). RITBE7 0167-8493
6. Erzin, E. et al., Multimodal person recognition for human-vehicle interaction., IEEE Multimedia. 13 (2), 18-31 (2006). IEMUE4 10.1109/MMUL.2006.37
7. Couture-Beil, A., R. Vaughan and G. Mori, Selecting and commanding individual robots in a vision-based multi-robot system., presented at the Seventh Canadian Conference on Computer and Robot Vision, Ottawa, Ontario, Canada, 31 May to 2 June 2010.
8. Wright, J. et al., Sparse representation for computer vision and pattern recognition., in Proc. IEEE, 1031-1044 (2010). IEEPAD 0018-9219 10.1109/JPROC.2010.2044470
9. Zhang, D. et al.., Advanced pattern recognition technologies with applications to biometrics., Hershey, Pa.: Medical Information Science Reference (2009).
10. Jin, Z. et al., Face recognition based on the uncorrelated discriminant transformation., Pattern Recognit. 34 (7), 1405-1416 (2001). 0031-3203 10.1016/S0031-3203(00)00084-4 (Pubitemid 32424906)
11. Belhumeur, P., J. Hespanda and D. Kriegman, Eigenfaces vs. fisherfaces: recognition using class specific linear projection., IEEE Trans. Pattern Anal. Mach. Intell. 19 (7), 711720 (1997). ITPIDJ 0162-8828 10.1109/34.598228
12. Xu, Y. et al., An approach for directly extracting features from matrix data and its application in face recognition., Neurocomputing 71 (10-12), 1857-1865 (2008). NRCGEO 0925-2312 10.1016/j.neucom.2007.09.021
13. Xu, Y. et al., An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments., Pattern Recognit. 37, 2091-2094 (2004). 0031-3203 10.1016/j.patcog.2004.02.016
14. Zuo, W. et al., BDPCA plus LDA: a novel fast feature extraction technique for face recognition., IEEE Trans. Syst. Man Cybern. B Cybern. 36 (4), 946-953 (2006). 1083-4419 10.1109/TSMCB.2005.863377 (Pubitemid 44178163)
15. Xu, Y., L. Yao and D. Zhang, Improving the interest operator for face recognition., Expert System With Applications 36 (6), 9719-9728 (2009). 10.1016/j.eswa.2009.02.032
16. Wright, J. et al., Robust face recognition via sparse representation., IEEE Trans. Pattern Anal. Mach. Intell. 31 (2), 210-227 (2009). ITPIDJ 0162-8828 10.1109/TPAMI.2008.79
17. Ren, C. X. and D. Q. Dai, Sparse representation by adding noisy duplicates for enhanced face recognition: an elastic net regularization approach., presented at the Chinese Conference on Pattern Recognition, 5-6 November 2009, Nanjing, China.
18. Elhamifar, E. and R. Vidal, Sparse subspace clustering., in Proc. IEEE Int. Conf. on Comput. Vis. and Patt. Recog., IEEE Computer Society, Miami, FL, USA (2009).
19. Shi, Y. et al., Sparse discriminant analysis for breast cancer biomarker identification and classification., Prog. Nat. Sci. 19 (11), 1635-1641 (2009). PNASEA 1002-0071 10.1016/j.pnsc.2009.04.013
20. Candes, E. and T. Tao, Near-optimal signal recovery from random projections: universal encoding strategies?, IEEE Trans Inform. Theory 52 (12), 5406 -5425 (2006). IETTAW 0018-9448 10.1109/TIT.2006.885507 (Pubitemid 46445381)
21. Candes, E., J. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements., Comm. Pure Appl. Math. 59 (8), 1207-1223 (2006). 0010-3640 10.1002/cpa.20124 (Pubitemid 43988295)
22. Shi, Y. et al., Gene selection and visualization based on sparse maximal margin features., Presented at the Chinese Conference on Pattern Recognition, Nanjing, China, 5-6 (November 2009).
23. Donoho, D., For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution., Comm. Pure Appl. Math. 59 (6), 797-829 (2006). 0010-3640 10.1002/cpa.20132 (Pubitemid 43667226)
24. Xu, Y., D. Zhang, Z. Jin, M. Li and J.-Y. Yang, A fast kernel-based nonlinear discriminant analysis for multi-class problems., Pattern Recognit. 39 (6), 1026-1033 (2006). 0031-3203 10.1016/j.patcog.2005.10.029
25. Muller, K.-R. et al., An introduction to kernel-based learning algorithms., IEEE Trans. Neural Netw. 12 (1), 181-201 (2001). ITNNEP 1045-9227 10.1109/72.914517 (Pubitemid 32371478)
26. Yang, S., Z. Liu and L. Jiao, Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction., Neurocomputing (2011). NRCGEO 0925-2312 10.1109/M2RSM.2011.5697374
27. Fan, M. et al., Sparse regularization for semi-supervised classification., Pattern Recognit. 44 (8), 1777-1784 (2011). 0031-3203 10.1016/j.patcog.2011.02.013
28. Yang, M. et al., Robust sparse coding for face recognition., Proc. of the IEEE Conference on Computer Vision and Pattern Recognit 2011, 625-632 (2011).
29. Wang, X.-L. et al., Salt-and-pepper noise removal based on image sparse representation., Opt. Eng. 50, 097007 (2011). OPEGAR 0091-3286 10.1117/1.3625416
30. Chen, G., G. Dudekm and L. A. Torres-Mndez, Scene reconstruction with sparse range data and intensity information., Opt. Eng. 50, 097002 (2011). OPEGAR 0091-3286 10.1117/1.3617455
31. Chen, S., X. Hong and C. J. Harris, Regression-based D-optimality experimental design for sparse kernel density estimation., Neurocomputing 73 (4-6), 727-739 (2010). NRCGEO 0925-2312
32. Xu, Y. et al., A method for speeding up feature extraction based on KPCA., Neurocomputing 70 (4-6), 1056-1061 (2007). NRCGEO 0925-2312 10.1016/j.neucom.2006.09.005 (Pubitemid 46043996)
33. Mika, S. et al., Fisher discriminant analysis with kernels., in Proc. IEEE Signal Process. Soc. Workshop, Neural Networks for Signal Processing IX, IEEE Signal Process Society, Madison, WI, USA, pp. 41-48 (1999). (Pubitemid 30510358)
34. http://cobweb.ecn.purdue.edu/~aleix/aleix-face-DB.html.
35. Yang, J. et al., Two-dimensional PCA: a new approach to appearance-based face representation and recognition., IEEE Trans. Pattern A Anal. Mech. Intell. 26 (1), 131-137 (2004). 0162-8828 10.1109/TPAMI.2004.1261097
36. AT&T Laboratories, The database of faces., 2002, http://www.cl.cam. ac.uk/research/dtg/attarchive/facedatabase.html (29 December 2011).
37. The Yale face database., http://cvc.yale.edu/projects/yalefaces/ yalefaces.html (29 December 2011).
38. Xu, Y. et al., A two-phase test sample sparse representation method for use with face recognition., IEEE Trans. Circuits Syst. Video Tech. 21 (9), 1255-1262 (2011). 1051-8215 10.1109/TCSVT.2011.2138790
39. Candes, E. and J. Romberg, l1-Magic: recovery of sparse signals via convex programming., Advances 1-19 (2005). 0741-9783