An Improved Illumination Normalization and Robust Feature Extraction Technique for Face Recognition Under Varying Illuminations

  • Jyotsna YadavEmail author
  • Navin rajpal
  • Rajesh Mehta
Research Article - Computer Engineering and Computer Science


Invariant feature extraction under diverse illuminations is challenging for face recognition. Light intensity variations in face images are predominant in large-scale features. In existing techniques, such features are truncated to achieve illumination-invariant features. However, salient features are lost during small-scale feature extraction that affect performance. Thus, objective in proposed work is to attain improved illumination normalization where large-scale and small-scale feature information is efficiently extracted for face recognition. First, reflectance ratio and histogram equalization (RRHE)-based new illumination normalization framework is proposed in which illumination deviations are annulled. Then, histogram equalization is employed to enhance contrast of reflectance ratio image by adjusting pixel intensities. Then, robust feature extraction in discrete wavelet packet transform domain (RFDWPT) from RRHE images is performed using various orthogonal wavelets with distinct vanishing moments. Here, small-scale features (noise effect) of RRHE images are discarded and final feature vector is formed by appropriate small-scale and large-scale features. This results in illumination-normalized salient features for eigen space analysis where nearest neighbor classification is performed on compact size training and test features. Compared with other face recognition techniques under different illumination conditions, significant enhancement in recognition accuracy is achieved on benchmark databases such as Yale B, CMU-PIE, Yale and extended Yale B.


Discrete wavelet packet transform Face recognition Illumination normalization Reflectance ratio Robust feature extraction 


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  1. 1.
    Zhao, W.; Chellappa, R.; Phillips, P.J.; Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Lahasan, B.; Lutfi, S.L.; San-Segundo, R.: A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression. Artif. Intell. Rev. 1–31 (2017).
  3. 3.
    Zhou, C.; Wang, L.; Zhang, Q.; Wei, X.: Face recognition based on PCA image reconstruction and LDA. Opt. Int. J. Light Electron Opt. 124(22), 5599–5603 (2013)CrossRefGoogle Scholar
  4. 4.
    Senthilkumar, R.; Gnanamurthy, R.K.: A comparative study of 2D PCA face recognition method with other statistically based face recognition methods. J. Inst. Eng. (India) Ser. B 97(3), 425–430 (2016)CrossRefGoogle Scholar
  5. 5.
    Zhang, S.; He, B.; Nian, R.; Wang, J.; Han, B.; Lendasse, A.; Yuan, G.: Fast image recognition based on independent component analysis and extreme learning machine. Cognit. Comput. 6(3), 405–422 (2014)CrossRefGoogle Scholar
  6. 6.
    Gu, G.; Hou, Z.; Chen, C.; Zhao, Y.: A dimensionality reduction method based on structured sparse representation for face recognition. Artif. Intell. Rev. 46(4), 431–443 (2016)CrossRefGoogle Scholar
  7. 7.
    Li, C.; Mi, J.X.: Sparse factorial code representation using independent component analysis for face recognition. Multimed. Tools Appl. 77(16), 1–22 (2018)Google Scholar
  8. 8.
    Goel, T.; Nehra, V.; Vishwakarma, V.P.: Comparative analysis of various illumination normalization techniques for face recognition. Int. J. Comput. Appl. 28(9), 1–11 (2011)Google Scholar
  9. 9.
    Tan, X.; Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Lee, P.; Wu, S.; Hung, Y.: Illumination compensation using oriented local histogram equalization and its application to face recognition. IEEE Trans. Image Process. 21(9), 4280–4289 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Liu, H.D.; Yang, M.; Gao, Y.; Cui, C.: Local histogram specification for face recognition under varying lighting conditions. Image Vis. Comput. 32(5), 335–347 (2014)CrossRefGoogle Scholar
  12. 12.
    Luo, Y.; Guan, Y.: Enhanced facial texture illumination normalization for face recognition. Appl. Opt. 54(22), 6887–6894 (2015)CrossRefGoogle Scholar
  13. 13.
    Zhang, T.; Tang, Y.Y.; Fang, B.; Shang, Z.; Liu, X.: Face recognition under varying illumination using gradient faces. IEEE Trans. Image Process. 18(11), 2599–2606 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wang, B.; Li, W.; Yang, W.; Liao, Q.: Illumination normalization based on Weber’s law with application to face recognition. IEEE Signal Process. Lett. 18(8), 462–465 (2011)CrossRefGoogle Scholar
  15. 15.
    Wu, Y.; Jiang, Y.; Zhou, Y.; Li, W.; Lu, Z.; Liao, Q.: Generalized Weber-face for illumination-robust face recognition. Neurocomputing 136, 262–267 (2014)CrossRefGoogle Scholar
  16. 16.
    Kim, W.; Suh, S.; Hwang, W.; Han, J.J.: SVD face: illumination-invariant face representation. IEEE Signal Process. Lett. 21(11), 1336–1340 (2014)CrossRefGoogle Scholar
  17. 17.
    Yu, Y.F.; Dai, D.Q.; Ren, C.X.; Huang, K.K.: Discriminative multi-layer illumination-robust feature extraction for face recognition. Pattern Recognit. 67, 201–212 (2017)CrossRefGoogle Scholar
  18. 18.
    Chen, W.; Er, M.J.; Wu, S.: 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)CrossRefGoogle Scholar
  19. 19.
    Vishwakarma, V.P.: Illumination normalization using fuzzy filter in DCT domain for face recognition. Int. J. Mach. Learn. Cybern. 6(1), 17–34 (2015)CrossRefGoogle Scholar
  20. 20.
    Cao, X.; Shen, W.; Yu, L.G.; Wang, Y.L.; Yang, J.Y.; Zhang, Z.W.: Illumination invariant extraction for face recognition using neighboring wavelet coefficients. Pattern Recognit. 45(4), 1299–305 (2012)CrossRefGoogle Scholar
  21. 21.
    Poon, B.; Amin, M.A.; Yan, H.: Performance evaluation and comparison of PCA Based human face recognition methods for distorted images. Int. J. Mach.Learn. Cybern. 2(4), 245–259 (2011)Google Scholar
  22. 22.
    Huang, Z.H.; Li, W.J.; Shang, J.; Wang, J.; Zhang, T.: Non-uniform patch based face recognition via 2D-DWT. Image Vis. Comput. 37, 12–19 (2015)CrossRefGoogle Scholar
  23. 23.
    Baradarani, A.; Wu, Q.J.; Ahmadi, M.: An efficient illumination invariant face recognition framework via illumination enhancement and DD-DTCWT filtering. Pattern Recognit. 46(1), 57–72 (2013)CrossRefGoogle Scholar
  24. 24.
    Selvakumar, K.; Jerome, J.; Rajamani, K.: Robust face identification using DTCWT and PCA subspace based sparse representation. Multimed. Tools Appl. 75(23), 16073–16092 (2016). CrossRefGoogle Scholar
  25. 25.
    Biswas, S.; and Jaya, S. : An efficient face recognition method using contourlet and curvelet transform. J. King Saud Univ. Comput. Inf. Sci. (2017).
  26. 26.
    AlZubi, S.; Jararweh, Y.; Shatnawi, R. :. Medical volume segmentation using 3d multiresolution analysis. In: Proceedings of International Conference on Innovations in Information Technology (IIT) (2012)Google Scholar
  27. 27.
    AlZubi, S.; Sharif, M. S.; Abbod, M :. Efficient implementation and evaluation of wavelet packet for 3d medical image segmentation. In: Proceedings of IEEE International Workshop on Medical Measurements and Applications (MeMeA), pp. 619–622 (2011)Google Scholar
  28. 28.
    Rezaee, H.; Aghagolzadeh; A.; Seyedarabi, M. H.; Al Zu’bi, S. : Tracking and occlusion handling in multi-sensor networks by particle filter. In: Proceedings of IEEE Conference and Exhibition (GCC), pp. 397–400 (2011)Google Scholar
  29. 29.
    Al Zu’bi S; Islam, N.; Abbod, M. : 3D Multiresolution Analysis for reduced features segmentation of medical volumes using PCA. In: Proceedings of IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pp. 604–607 (2010)Google Scholar
  30. 30.
    AlZu’bi, S.; Amira, A.: 3D medical volume segmentation using hybrid multiresolution statistical approaches. Adv. Artif. Intell. 2010, 1–15 (2010)CrossRefGoogle Scholar
  31. 31.
    Sable, A.H.; Talbar, S.N.: A Novel Illumination invariant Face recognition method based on PCA and WPD using YCbCr color space. Proc. Comput. Sci. 92, 181–187 (2016)CrossRefGoogle Scholar
  32. 32.
    Wenjing, T.; Fei, G.; Renren, D.; Yujuan, S.; Ping, L.: Face recognition based on the fusion of wavelet packet sub-images and fisher linear discriminant. Multimed. Tools Appl. 76(21), 22725–22740 (2017)CrossRefGoogle Scholar
  33. 33.
    Jyotsna; Rajpal, N.; Virendra P. Vishwakarma : Face recognition using Symlet, PCA and cosine angle distance measure. In: Proceedings of IEEE Ninth International Conference on Contemporary Computing (IC3), pp. 1–7 (2016)Google Scholar
  34. 34.
    Horn, B.K.P.H.: Robot Vision, pp. 130–132. McGraw Hill, New York (1986)Google Scholar
  35. 35.
    Nayar, S.K.; Bolle, R.M.: Reflectance based object recognition. Int. J. Comput. Vis. 17(3), 219–240 (1996)CrossRefGoogle Scholar
  36. 36.
    Huang, Z.H.; Li, W.J.; Wang, J.; Zhang, T.: Face recognition based on pixel-level and feature-level fusion of the top-level’s wavelet sub-bands. Inf. Fusion 22, 95–104 (2015)CrossRefGoogle Scholar
  37. 37.
    Poon, B.; Amin, M.A.; Yan, H.: Performance evaluation and comparison of PCA Based human face recognition methods for distorted images. Int. J. Mach. Learn. Cybern. 2(4), 245–259 (2011)CrossRefGoogle Scholar
  38. 38.
    Eyupoglu, C. : Implementation of color face recognition using PCA and k-NN classifier. In : Proceedings of IEEE Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), NW Russia, pp. 199–202 (2016)Google Scholar
  39. 39.
    López, J.; Maldonado, S.: Redefining nearest neighbor classification in high-dimensional settings. Pattern Recognit. Lett. 110, 36–43 (2018)CrossRefGoogle Scholar
  40. 40.
    Yale B face database (Online). Accessed 24 June 2012
  41. 41.
    Extended Yale B face database (Online).*leekc/ExtYaleDatabase/ExtYaleB.html. Accessed 10 May 2014
  42. 42.
    Yale face database (Online). Accessed 06 Sept 2017
  43. 43.
    Sim, T.; Baker, S.; Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1615–1618 (2003)CrossRefGoogle Scholar

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© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.University School of Information and Communication TechnologyGuru Gobind Singh Indraprastha UniversityDwarkaIndia
  2. 2.Computer Science and Engineering DepartmentThapar Institute of Engineering and TechnologyPatialaIndia

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