An experimental study for the effects of noise on face recognition algorithms under varying illumination

  • Guang Yi ChenEmail author


When the illumination changes, the appearance of facial images will change dramatically. Lighting changes make face recognition a very challenging and difficult job. In addition, the effects of noise on existing face recognition methods have been neglected in the literature, to the best of our knowledge. In this work, we study the effects of noise on existing illumination-invariant face recognition methods. We tested such noise as Gaussian white noise, Poisson noise, salt & pepper noise, speckle noise, etc. In total, 21 methods have been included in this study in this work. We find out that, when noise is added to facial images, Tan and Triggs’ method achieves the best results for both the extended Yale B face database and the CMU-PIE face database. When facial images do not contain noise, isotropic smoothing is preferred because it obtains the highest average recognition rate (96%) for the extended Yale B face database and 16 methods obtain perfect correct recognition rates (100%) for the CMU-PIE face database.


Gaussian white noise Poisson noise salt & pepper noise nearest neighbour classifier illumination invariant Face recognition 



The authors thank Dr. Vitomir Struc for posting his Inface toolbox for illumination invariant face recognition, and the owners of the extended Yale-B and the CMU-PIE face databases for sharing their databases with us.

Compliance with ethical standards

Conflict of interests

The authors declare that there is no conflict of interests for this paper.


  1. 1.
    Chen GY, Bui TD, Krzyzak A (2009) Invariant pattern recognition using radon, dual-tree complex wavelet and Fourier transforms. Pattern Recogn 42(9):2013–2019CrossRefzbMATHGoogle Scholar
  2. 2.
    Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition Using Discrete Cosine Transform in Logarithmic Domain. IEEE Trans Syst Man Cybern B 36(2):458–466CrossRefGoogle Scholar
  3. 3.
    Chen GY, Xie WF (2011) Wavelet-based moment invariants for pattern recognition. Opt Eng 50(7):077205CrossRefGoogle Scholar
  4. 4.
    Chen GY, Xie WF (2016) A Comparative study for the effects of noise on illumination invariant face recognition algorithms. Proceedings of the twelfth international conference on intelligent computing (ICIC), Lanzhou, ChinaGoogle Scholar
  5. 5.
    Davidson MW, Abramowitz M. Molecular expressions microscopy primer: digital image processing – Difference of Gaussians Edge Enhancement Algorithm. Olympus America Inc., and Florida State University. Available at:
  6. 6.
    Dewantara BSB, Miura J (2016) OptiFuzz: a robust illumination invariant face recognition system and its implementation. Mach Vis Appl 27:877–891CrossRefGoogle Scholar
  7. 7.
    Du S, Ward RK (2010) Adaptive region-based image enhancement method for robust face recognition under variable illumination conditions. IEEE Transactions on Circuits and Systems for Video Technology 20(9):1165–1175CrossRefGoogle Scholar
  8. 8.
    Faraji MR, Qi J (2015) Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns. IET Comput Vis 9(3):390–399CrossRefGoogle Scholar
  9. 9.
    Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906CrossRefGoogle Scholar
  10. 10.
    Gross R, Brajovic V (2003) An image preprocessing algorithm for illumination invariant face recognition. In: Proc. of the 4th International Conference on Audio- and Video-Based Biometric Personal Authentication, AVPBA’03, pp. 10–18Google Scholar
  11. 11.
    Han H, Shan S, Chen X, Gao W (2013) A comparative study on illumination preprocessing in face recognition. Pattern Recogn 46(6):1691–1699CrossRefGoogle Scholar
  12. 12.
    Heusch G, Cardinaux F, Marcel S (2005) Lighting normalization algorithms for face verification,” IDIAP-com 05–03Google Scholar
  13. 13.
    Hu H (2015) Illumination invariant face recognition based on dual-tree complex wavelet transform. IET Comput Vis 9(2):163–173CrossRefGoogle Scholar
  14. 14.
    Jabson DJ, Rahmann Z, Woodell GA (1997) A multiscale Retinex for bridging the gap between color images and the human observations of scenes. IEEE Trans Image Process 6(7):897–1056Google Scholar
  15. 15.
    Lai ZR, Dai DQ, Ren CX, Huang KK (2015) Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans Image Process 24(6):1735–1747MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698CrossRefGoogle Scholar
  17. 17.
    Leng L, Zhang J, Khan MK, Chen X, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. International journal of the Physical Sciences 5(17):2543–2554Google Scholar
  18. 18.
    Leng L, Zhang J, Khan MK, Chen X, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. 2010 International Conference on Information and Communication Technology Convergence (ICTC), pp. 467–471Google Scholar
  19. 19.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data. Proceedings of the twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 1617–1623Google Scholar
  20. 20.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  21. 21.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune Teller: Predicting Your Career Path. Thirtieth AAAI Conference on Artificial Intelligence, pp. 201–207Google Scholar
  22. 22.
    Nikan S, Ahmadi M (2015) Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion. IET Image Process 9(1):12–21CrossRefGoogle Scholar
  23. 23.
    Oppenheim AV, Schafer RW, Stockham TG (1968) Nonlinear filtering of multiplied and convolved signals. Proc IEEE 56(8):1264–1291CrossRefGoogle Scholar
  24. 24.
    Park YK, Park SL, Kim JK (2008) Retinex method based on adaptive smoothing for illumination invariant face recognition. Signal Process 88(8):1929–1945CrossRefzbMATHGoogle Scholar
  25. 25.
    Ramaiah NP, Ijjina EP, Mohan CK (2015) Illumination invariant face recognition using convolutional neural networks. IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1–4Google Scholar
  26. 26.
    Roy H, Bhattacharjee D (2016) Local-Gravity-Face (LG-face) for illumination-invariant and heterogeneous face recognition. IEEE Transactions on Information Forensics and Security 11(7)Google Scholar
  27. 27.
    Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618CrossRefGoogle Scholar
  28. 28.
    Struc V, Pavesic N (2009) Illumination invariant face recognition by non-local smoothing. Proceedings of BIOID MultiComm, LNCS 5707, Springer, pp. 1–8Google Scholar
  29. 29.
    Tan X, Triggs B (2010) Enhanced local texture sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Wang H, Li SZ, Wang Y, Zhang J (2004) Self quotient image for face recognition. In: Proc. of the International Conference on Pattern Recognition, pp. 1397–1400Google Scholar
  31. 31.
    Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on Weber's law with application to face recognition. IEEE Signal Processing Letters 18(8):462–465CrossRefGoogle Scholar
  32. 32.
    Xie X, Zheng WS, Lai J, Yuen PC, Suen CY (2011) Normalization of face illumination based on large-and small-scale features. IEEE Trans Image Process 20(7):1807–1821MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Zhang T, Fang 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):252–258CrossRefzbMATHGoogle Scholar
  34. 34.
    Zhang T, Tang YY, Fang B, Shang Z, Liu X (2009) Face recognition under varying illumination using gradient faces. IEEE Trans Image Process 18(11):2599–2606MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Zou X, Kittler J, Messer K (2007) Illumination invariant face recognition: a survey. In: Proceedings of the Biometrics: Theory, Applications, and Systems, pp. 1–8Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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