Skip to main content
SpringerLink
Log in

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

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  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). https://doi.org/10.1007/s10462-017-9578-y

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  12. Luo, Y.; Guan, Y.: Enhanced facial texture illumination normalization for face recognition. Appl. Opt. 54(22), 6887–6894 (2015)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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). https://doi.org/10.1016/j.jksuci.2017.10.010

    Article  Google Scholar 

  25. Biswas, S.; and Jaya, S. : An efficient face recognition method using contourlet and curvelet transform. J. King Saud Univ. Comput. Inf. Sci. (2017). https://doi.org/10.1016/j.jksuci.2017.10.010

  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)

  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)

  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)

  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)

  30. AlZu’bi, S.; Amira, A.: 3D medical volume segmentation using hybrid multiresolution statistical approaches. Adv. Artif. Intell. 2010, 1–15 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  34. Horn, B.K.P.H.: Robot Vision, pp. 130–132. McGraw Hill, New York (1986)

    Google Scholar 

  35. Nayar, S.K.; Bolle, R.M.: Reflectance based object recognition. Int. J. Comput. Vis. 17(3), 219–240 (1996)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  39. López, J.; Maldonado, S.: Redefining nearest neighbor classification in high-dimensional settings. Pattern Recognit. Lett. 110, 36–43 (2018)

    Article  Google Scholar 

  40. Yale B face database (Online). http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html. Accessed 24 June 2012

  41. Extended Yale B face database (Online). http://vision.ucsd.edu/*leekc/ExtYaleDatabase/ExtYaleB.html. Accessed 10 May 2014

  42. Yale face database (Online). http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html. Accessed 06 Sept 2017

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

    Jyotsna Yadav & Navin rajpal

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Rajesh Mehta

Authors
  1. Jyotsna Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Navin rajpal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajesh Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jyotsna Yadav.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, J., rajpal, N. & Mehta, R. An Improved Illumination Normalization and Robust Feature Extraction Technique for Face Recognition Under Varying Illuminations. Arab J Sci Eng 44, 9067–9086 (2019). https://doi.org/10.1007/s13369-019-03729-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-019-03729-6

Keywords

Search

Navigation