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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 8, Iss. 6 — Jun. 27, 2013

A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion

Yi Zhou, Sheng-Tong Zhou, Zuo-Yang Zhong, and Hong-Guang Li  »View Author Affiliations


Optics Express, Vol. 21, Issue 9, pp. 11294-11308 (2013)
http://dx.doi.org/10.1364/OE.21.011294


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Abstract

Almost all the face recognition algorithms are unsatisfied due to illumination variation. Feature with high frequency represents the face intrinsic structure according to the common assumption that illumination varies slowly and the face intrinsic feature varies rapidly. In this paper, we will propose an adaptive scheme based on FBEEMD and detail feature fusion. FBEEMD is a fast version of BEEMD without time-consuming surface interpolation and iteration computation. It can decompose an image into sub-images with high frequency matching detail feature and sub-images with low frequency corresponding to contour feature. However, it is difficult to determine by quantitative analysis that which sub-images with high frequency can be used for reconstructing an illumination-invariant face. Thus, two measurements are proposed to calculate weights for quantifying the detail feature. With this fusion technique, one can reconstruct a more illumination-neutral facial image to improve face recognition rate. Verification experiments using classical recognition algorithms are tested with Yale B, PIE and FERET databases. The encouraging results show that the proposed scheme is very effective when dealing with face images under variable lighting condition.

© 2013 OSA

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.2980) Image processing : Image enhancement
(100.3010) Image processing : Image reconstruction techniques
(100.5010) Image processing : Pattern recognition
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

History
Original Manuscript: December 27, 2012
Revised Manuscript: March 26, 2013
Manuscript Accepted: March 30, 2013
Published: May 1, 2013

Virtual Issues
Vol. 8, Iss. 6 Virtual Journal for Biomedical Optics

Citation
Yi Zhou, Sheng-Tong Zhou, Zuo-Yang Zhong, and Hong-Guang Li, "A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion," Opt. Express 21, 11294-11308 (2013)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-21-9-11294


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References

  1. R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: a survey,” Proc. IEEE83(5), 705–741 (1995). [CrossRef]
  2. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A literature survey,” ACM Comput. Surv.35(4), 399–458 (2003). [CrossRef]
  3. M. Park, C.-W. Park, M. Park, and C.-H. Lee, “Algorithm for detecting human faces based on convex-hull,” Opt. Express10(6), 274–279 (2002). [CrossRef] [PubMed]
  4. M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cogn. Neurosci.3(1), 71–86 (1991). [CrossRef]
  5. L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” J. Opt. Soc. Am. A4(3), 519–524 (1987). [CrossRef] [PubMed]
  6. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell.19(7), 711–720 (1997). [CrossRef]
  7. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Netw.13(6), 1450–1464 (2002). [CrossRef] [PubMed]
  8. C. Liu and H. Wechsler, “Independent component analysis of Gabor features for face recognition,” IEEE Trans. Neural Netw.14(4), 919–928 (2003). [CrossRef] [PubMed]
  9. P. S. Penev and J. J. Atick, “Local feature analysis: a general statistical theory for object representation,” Network-Comp Neural.7(3), 477–500 (1996). [CrossRef]
  10. L. Wiskott, J. M. Fellous, N. Kuiger, and C. von der Malsburg, “Face recognition by elastic bunch graph matching,” IEEE Trans. Pattern Anal. Mach. Intell.19(7), 775–779 (1997). [CrossRef]
  11. M. J. Er, S. Wu, J. Lu, and H. L. Toh, “Face recognition with radial basis function (RBF) neural networks,” IEEE Trans. Neural Netw.13(3), 697–710 (2002). [CrossRef] [PubMed]
  12. A. Lanitis, C. J. Taylor, and T. F. Cootes, “Automatic face identification system using flexible appearance models,” Image Vis. Comput.13(5), 393–401 (1995). [CrossRef]
  13. V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Trans. Pattern Anal. Mach. Intell.25(9), 1063–1074 (2003). [CrossRef]
  14. V. N. Ara and H. H. Monson, “Face Recognition Using An Embedded HMM,” in Proceedings of IEEE Conference on Audio and Video-based Biometric Person Authentication, pp. 19–24. (1999).
  15. G. Guo, S. Z. Li, and K. L. Chan, “Support vector machines for face recognition,” Image Vis. Comput.19(9-10), 631–638 (2001). [CrossRef]
  16. B. Guo, K.-M. Lam, K.-H. Lin, and W.-C. Siu, “Human face recognition based on spatially weighted Hausdorff distance,” Pattern Recognit. Lett.24(1-3), 499–507 (2003). [CrossRef]
  17. Y. Gao and M. K. H. Leung, “Face recognition using line edge map,” IEEE Trans. Pattern Anal. Mach. Intell.24(6), 764–779 (2002). [CrossRef]
  18. J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition,” IEEE Trans. Pattern Anal. Mach. Intell.27(2), 230–244 (2005). [CrossRef] [PubMed]
  19. S. K. Zhou and R. Chellappa, “Image-based face recognition under illumination and pose variations,” J. Opt. Soc. Am. A22(2), 217–229 (2005). [CrossRef] [PubMed]
  20. L. Cao, Q. He, C. Ouyang, Y. Liao, and G. Jin, “Improvement to human-face recognition in a volume holographic correlator by use of speckle modulation,” Appl. Opt.44(4), 538–545 (2005). [CrossRef] [PubMed]
  21. A. Alfalou and C. Brosseau, “Robust and discriminating method for face recognition based on correlation technique and independent component analysis model,” Opt. Lett.36(5), 645–647 (2011). [CrossRef] [PubMed]
  22. Y. Liao, Y. Guo, L. Cao, X. Ma, Q. He, and G. Jin, “Experiment on parallel correlated recognition of 2030 human faces based on speckle modulation,” Opt. Express12(17), 4047–4052 (2004). [CrossRef] [PubMed]
  23. J. García, J. Valles, and C. Ferreira, “Detection of three-dimensional objects under arbitrary rotations based on range images,” Opt. Express11(25), 3352–3358 (2003). [CrossRef] [PubMed]
  24. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell.23(6), 643–660 (2001). [CrossRef]
  25. A. Mian, “Illumination invariant recognition and 3D reconstruction of faces using desktop optics,” Opt. Express19(8), 7491–7506 (2011). [CrossRef] [PubMed]
  26. H. Song, S. Lee, J. Kim, and K. Sohn, “Three-dimensional sensor-based face recognition,” Appl. Opt.44(5), 677–687 (2005). [CrossRef] [PubMed]
  27. P. K. Baheti and M. A. Neifeld, “Adaptive feature-specific imaging: a face recognition example,” Appl. Opt.47(10), B21–B31 (2008). [CrossRef] [PubMed]
  28. Y. Kim, J. Na, S. Yoon, and J. Yi, “Masked fake face detection using radiance measurements,” J. Opt. Soc. Am. A26(4), 760–766 (2009). [CrossRef] [PubMed]
  29. A. Shashua and T. Riklin-Raviv, “The quotient image: class-based re-rendering and recognition with varying illuminations,” IEEE Trans. Pattern Anal. Mach. Intell.23(2), 129–139 (2001). [CrossRef]
  30. M. Savvides and B. V. K. V. Kumar, “Illumination Normalization Using Logarithm Transforms for Face Authentication,” in Lecture Notes in Computer Science (Springer-Verlag, Berlin, 2003), pp. 549–556.
  31. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive Histogram Equalization and Its Variations,” Comput. Vis. Graph. Image Process.39(3), 355–368 (1987). [CrossRef]
  32. X. Xie and K.-M. Lam, “Face recognition under varying illumination based on a 2D face shape model,” Pattern Recognit.38, 221–230 (2005).
  33. W. Zhao and R. Chellappa, “Illumination-insensitive face recognition using symmetric shape-from-shading,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Hilton Head Island, South Califonia, 2000), pp. 286–293.
  34. W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,” IEEE Trans. Syst. Man Cybern. Part B-Cybern.36(2), 458–466 (2006). [CrossRef]
  35. S. L. Wijaya, M. Savvides, and B. V. Vijaya Kumar, “Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices,” Appl. Opt.44(5), 655–665 (2005). [CrossRef] [PubMed]
  36. M. Shao, Y. Wang, and X. Ling, “A BEMD based normalization method for face recognition under variable illuminations,” in Proceedings of IEEE Conference on Acoustics Speech and Signal Processing (ICASSP), (Dallas, Texas, 2010), pp. 1114–1117. [CrossRef]
  37. Z. Wu, N. E. Huang, and X. Chen, “The Multi-dimensional ensemble empirical mode decomspostion method,” Adv.Adapt. Data Anal.1(03), 339–372 (2009). [CrossRef]
  38. J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput.21(12), 1019–1026 (2003). [CrossRef]
  39. J. C. Nunes, S. Guyot, and E. Deléchelle, “Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition,” Mach. Vis. Appl.16, 177–188 (2005). [CrossRef]
  40. J. C. Nunes, O. Niang, Y. Bouaoune, E. Delechelle, and P. Bunel, “Bidimensional empirical mode decomposition modified for texture analysis,” in Proceedings of Image Analysis, J. Bigun, and T. Gustavsson, eds. (Springer, Berlin, 2003), pp. 171–177.
  41. X. Zhou, A. G. Podoleanu, Z. Yang, T. Yang, and H. Zhao, “Morphological operation-based bi-dimensional empirical mode decomposition for automatic background removal of fringe patterns,” Opt. Express20(22), 24247–24262 (2012). [CrossRef] [PubMed]
  42. X. Zhou, T. Yang, H. Zou, and H. Zhao, “Multivariate empirical mode decomposition approach for adaptive denoising of fringe patterns,” Opt. Lett.37(11), 1904–1906 (2012). [CrossRef] [PubMed]
  43. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Adv. Adapt. Data Anal.1(01), 1–41 (2009). [CrossRef]
  44. X. Zhou, H. Zhao, and T. Jiang, “Adaptive analysis of optical fringe patterns using ensemble empirical mode decomposition algorithm,” Opt. Lett.34(13), 2033–2035 (2009). [CrossRef] [PubMed]
  45. Y. Zhou and H. Li, “Adaptive noise reduction method for DSPI fringes based on bi-dimensional ensemble empirical mode decomposition,” Opt. Express19(19), 18207–18215 (2011). [CrossRef] [PubMed]
  46. A. Linderhed, “Variable sampling of the empirical mode decomposition of two-dimensional signals,” Int. J. Wavelets Multi.3(03), 435–452 (2005). [CrossRef]
  47. Z. Liu and S. Peng, “Boundary Processing of bidimensional EMD using texture synthesis,” IEEE Signal Process. Lett.12(1), 33–36 (2005). [CrossRef]
  48. S. M. A. Bhuiyan, R. R. Adhami, and J. F. Khan, “Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation,” EURASIP J. Adv. Signal Process.2008(164), 725356 (2008).
  49. S. M. A. Bhuiyan, R. R. Adhami, and J. F. Khan, “A novel approach of fast and adaptive bidimensional empirical mode decomposition,” in Processings of IEEE international Conference on Acoustics, Speech and Signal Processing (Institute of Electrical and Electronics Engineers, 2008), pp. 1313–1316. [CrossRef]
  50. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A454(1971), 903–995 (1998). [CrossRef]
  51. N. E. Huang, M.-L. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen, and K. L. Fan, “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis,” Proc. R. Soc. Lond. A459(2037), 2317–2345 (2003). [CrossRef]
  52. K. Patorski, K. Pokorski, and M. Trusiak, “Fourier domain interpretation of real and pseudo-moiré phenomena,” Opt. Express19(27), 26065–26078 (2011). [CrossRef] [PubMed]
  53. E. H. Land and J. J. McCann, “Lightness and Retinex Theory,” J. Opt. Soc. Am.61(1), 1–11 (1971). [CrossRef] [PubMed]
  54. H. Wang, S. Z. Li, and Y. Wang, “Face recognition under varying lighting conditions using self quotient image,” in Proceedings of IEEE Conference on Automatic Face and Gesture Recognition (Institute of Electrical and Electronics Engineers, 2004), pp. 819–824.
  55. T. Sim, S. Baker, and M. Bsat, “The CMU Pose, Illumination, and Expression (PIE) database,” in Proceedings of IEEE Conference on Automatic Face and Gesture Recognition (Institute of Electrical and Electronics Engineers, 2002), pp. 46–51. [CrossRef]
  56. P. J. Philips, H. Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” Image Vis. Comput.16, 295–306 (1998). [CrossRef]

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