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An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition

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Abstract

This paper presents an efficient hybrid DWT-DCT based illumination normalization technique for face recognition. In a face image, illumination usually changes slowly compared to the reflectance except some casting shadows and specularities on the face. Consequently, illumination variations mainly lie in the low frequency band of the face image. Therefore, in the present work, low frequency coefficients are processed to nullify the effect of illumination variations. Discrete wavelet transform (DWT) is used to decompose the image into frequency domain. It is a sub-band coding technique which decomposes image into four sub-bands: low-low (LL), low-high (LH), high-low (HL) and high-high (HH). As illumination is related to low frequency coefficients, normalization is mainly performed on LL sub-band rather than the whole face. The fuzzy filter is applied on the appropriate number of low frequency discrete Cosine transform (DCT) coefficients of LL sub-band to minimize the variations under different lighting conditions. Also, minor corrections are performed on the rest three sub-bands. After modification, the normalized LL sub-band and rest three sub-bands are combined to generate the normalized face image. The given approach achieves zero error rates on Yale B and CMU PIE face database. Also, good performance results have been achieved on Extended Yale B face database. These results clearly confirm the effectiveness of the given approach of illumination normalization.

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References

  1. Adini Y, Moses Y, Ullman S (1997) Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans Pattern Anal Mach Intell 19(7):721–732

    Article  Google Scholar 

  2. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  3. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  4. Belhumeur PN, Kriegman DJ (1998) What is the set of images of an object under all possible illumination conditions. Int J Comput Vis 28(3):245–260

    Article  Google Scholar 

  5. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  6. Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):705–740

    Article  Google Scholar 

  7. Chen HF, Belhumeur PN, Jacobs DW (2000) In search of illumination invariants. Proc IEEE Conf Comput Vis Pattern Recognit 1:254–261

    Google Scholar 

  8. Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man Cybern B Cybern 36(2):458–466

    Article  Google Scholar 

  9. Cohen A, Ryan RD (1995) Wavelets and multiscale signal processing. Chapman & Hall, London

    Book  MATH  Google Scholar 

  10. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  MATH  Google Scholar 

  11. Delac K, Grgic Kos T (2006) Sub-image homomorphic filtering technique for improving facial identification under difficult illumination conditions. International Conference on Systems, Signals and Image Processing (IWSSIP’06) 19:1519–1524

    Google Scholar 

  12. Etemad K, Chellapa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14(8):1724–1733

    Article  Google Scholar 

  13. Faraji MR, Qi X (2014) Face recognition under varying illumination with logarithmic fractal analysis. IEEE Signal Processing Letters 21(12):1457–1461. https://doi.org/10.1109/LSP.2014.2343213

    Article  Google Scholar 

  14. Faraji MR, Qi X (2015a) Face recognition under illumination variations based on eight local directional patterns. IET Biometrics 4(1):10–17

    Article  Google Scholar 

  15. Faraji MR, Qi X (2015b) Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns. IET Comput Vis 9(3):390–399

    Article  Google Scholar 

  16. Gao Y, Leung M (2002) Face recognition using line edge map. IEEE Transactions on PAMI 24(6):764–779

    Article  Google Scholar 

  17. Georghiades AS, Belhumeur PN, Jacobs DW (2001) 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

    Article  Google Scholar 

  18. Gonzalez RC, Woods RE (2006) Digital image processing. Pearson Education

  19. Jabid T, Kabir M, Chae O (2010) Local directional pattern for face recognition. In: Digest of Technical Papers International Conference on Consumer Electronics 329–330. https://doi.org/10.1109/ICCE.2010.5418801

  20. Jain AK (2006) Fundamentals of digital image processing. Pearson Education

  21. Jobson DJ, Rahman Z, Woodel GA (1997) Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing: special issue on color processing 6:451–462

    Article  Google Scholar 

  22. Juneja K, Verma A and Goel S (2015) An improvement on face recognition rate using local tetra patterns with support vector machine under varying illumination conditions. In: International Conference on Computing, Communication Automation (ICCCA):1079–1084. https://doi.org/10.1109/CCAA.2015.7148566

  23. Kim DJ, Shon MK, Lee S, Kim E (2016) Illumination robust face recognition approach using enhanced preprocessing and feature extraction. J Nanoelectron Optoelectron 11(2):141–147. https://doi.org/10.1166/jno.2016.1855

    Article  Google Scholar 

  24. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and application. Prentice Hall, New Jersey

    MATH  Google Scholar 

  25. Lai Z, Dai D, Ren C, Huang K (2015) Multi scale logarithm difference edge maps for face recognition against varying lighting conditions. IEEE Trans Image Process 24(6):1735–1747. https://doi.org/10.1109/TIP.2015.2409988

    Article  MathSciNet  MATH  Google Scholar 

  26. Land EH, McCann JJ (1971) Lightness and Retinex theory. J Opt Soc Am 61(1):1–11

    Article  Google Scholar 

  27. Lei Z, Pietikainen M, Li S (2014) Learning discriminant face descriptor. IEEE Trans Pattern Anal Mach Intell 36(2):289–302. https://doi.org/10.1109/TPAMI.2013.112

    Article  Google Scholar 

  28. Li SZ, Jain AK (2005) Handbook of face recognition. Springer

  29. Lin CC, Lin WC (1996) Extracting facial features by PCA. Pattern Recogn 29(12)

  30. MATLAB Reference Manual (2004) http://www.mathworks.com/access/helpdesk/help/toolbox/images

  31. Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to Kernel-based learning algorithms. IEEE Trans Neural Netw 12(2)

  32. Phillips PJ, Grother P, Micheals RJ, Blackburn DM, Tabassi E, Bone JM, FRVT (2002) Evaluation report, (2003) [Online]Available: http://www.frvt.org/FRVT2002/

  33. Phillips P J, Scruggs WT, Toole AJO, Flynn PJ, Bowyer KW, Schott CL, Sharpe M (2007) FRVT. (2006) and ICE. (2006) large-scale results. National Institute of Standards and Technology, NISTIR. 7408

  34. Proceedings of the International Conference on audio and video based person authentication. 1997–1999

  35. Proceedings of the International conferences on automatic face and gesture recognition. 1995–1998

  36. Ramirez A, Castillo R, Chae O (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752. https://doi.org/10.1109/TIP.2012.2235848

    Article  MathSciNet  MATH  Google Scholar 

  37. Revathy N, Guhan T (2012) Face recognition system using Back propagation artificial neural networks. IJAET 3(1):321–324

    Google Scholar 

  38. Ronny T, Wanquan V, Svetha V (2004) Application of the DCT energy histogram for face recognition. ICITA 4:305–310

    Google Scholar 

  39. Scholkopf B, Smola AJ, Muller KR (1998) Non-linear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319

    Article  Google Scholar 

  40. Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. IEEE International Workshop on AMFG 157–164

  41. Shashua A, Riklin T (2001) The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139

    Article  Google Scholar 

  42. Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database. Proc. Conf. Automatic Face Gesture Recognition 53–58

  43. Syafeeza AR, Khalil-Hani M, Liew SS, Bakhteri R (2014) Convolutional neural network for face recognition with pose and illumination variation. International Journal of Engineering and Technology (IJET) 6(1):44–57

    Google Scholar 

  44. The Extended Yale Face Database B, available [online]: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

  45. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–96

    Article  Google Scholar 

  46. Veenman CJ, Reinders MJT (2005) The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier. IEEE Trans Pattern Anal Mach Intell 23(9):1417–1429

    Article  Google Scholar 

  47. Vishwakarma VP, Pandey S, Gupta MN (2009) Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization. International Journal of Recent Trends in Engineering 1(1):318–322

    Google Scholar 

  48. Vishwakarma VP, Pandey S, Gupta MN (2010) An illumination invariant accurate face recognition with down scaling of DCT coefficients. J Comput Inf Technol 18(1):53–67

    Article  Google Scholar 

  49. Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on weber's law with application to face recognition. Signal Processing Letters 18(8):462–465. https://doi.org/10.1109/LSP.2011.2158998

    Article  Google Scholar 

  50. Wu Y, Jiang Y, Zhou Y, Li W, Lu Z, Liao Q (2014) Generalized weber-face for illumination-robust face recognition. Neurocomputing 136:262–267. https://doi.org/10.1016/j.neucom

    Article  Google Scholar 

  51. Xie X, Zheng WS, Lai J, Yuen PC (2011) Face illumination normalization on large and small scale features. IEEE Trans Image Process 20(7):1807–1821

    Article  MathSciNet  MATH  Google Scholar 

  52. Yale Face Database B (2001) http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html

  53. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  54. Zhang L, Samaras D. Face recognition under variable lighting using harmonic image exemplars. Proc IEEE Conf Comput Vis Pattern Recognit 1:19–25

  55. Zhang T, Gang B, Yuan Y, Tang YY, Shang Z, Li D, Lang F (2009) Multiscale facial structure representation for face recognition under varying illumination. Pattern Recogn 42(2):251–258

    Article  MATH  Google Scholar 

  56. Zhao W, Chellappa R (2001) Symmetric shape-from-shading using self-ratio image. Proceedings in IEEE International Conference on Computer Vision 55–75

  57. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

  58. Zhao J, Su Y, Wang D, Luo S (2003) Illumination ratio image: synthesizing and recognition with varying illuminations. Pattern Recogn Lett 24:2703–2710

    Article  Google Scholar 

  59. Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384

    Article  Google Scholar 

  60. Zou X, Kittler J, Messer K (2007) Illumination invariant face recognition: a survey. 1st IEEE International Conference on Biometric Theory, Application and Systems 113–120

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Correspondence to Virendra P. Vishwakarma.

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Vishwakarma, V.P., Goel, T. An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition. Multimed Tools Appl 78, 15213–15233 (2019). https://doi.org/10.1007/s11042-018-6837-0

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