Low-Light Face Image Enhancement Based on Dynamic Face Part Selection

  • Adel OulefkiEmail author
  • Mustapha Aouache
  • Messaoud Bengherabi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)


A common challenge faced by face recognition community is struggling to circumvent face images that are acquired under low-light situation. The present work aims to couple the power of the popular CLAHE algorithm for face preprocessing with a Fuzzy inference system in such a way to correct the annoyance of non-uniform illumination of face images in a targeted and a precise manner. Due to the particularity of the low-light illumination problem. Firstly, the input face image is divided into two equal sub-regions. Subsequently, the degree of brightness in each sub-region and in the whole face is used for dynamic decision of whether to normalize. In the case where only one region of the face undertakes the CLAHE-Fuzzy approach is applied. Thus, the left and right face regions are grouped back followed by further processing like a blur removal and contrast enhancement (smoothing). Visual results showed that more facial features appeared in comparison with other approaches for enhancement. Besides, we quantitatively validate the accuracy of the developed Partial Fuzzy Enhancement Approach (PFEA) with four different metrics. The effectiveness of PFEA technique has been demonstrated by presenting extensive experimental results using Extended Yale-B, CMU-PIE, Mobio, and CAS-PEAL databases.


Face image enhancement Partial Fuzzy Enhancement Approach (PFEA) Blending images 


  1. 1.
    Abbadi, B., Oulefki, A., Mostefai, M.: Development and implementation of a new dynamic face detection operator. Int. J. Comput. Appl. 49(20) (2012)CrossRefGoogle Scholar
  2. 2.
    Agaian, S., Roopaei, M., Shadaram, M., Bagalkot, S.S.: Bright and dark distance-based image decomposition and enhancement. In: 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, pp. 73–78. IEEE (2014)Google Scholar
  3. 3.
    Burt, P.J., Adelson, E.H.: A multiresolution spline with application to image mosaics. ACM Trans. Graph. 2(4), 217–236 (1983)CrossRefGoogle Scholar
  4. 4.
    Chang, Y., Jung, C., Ke, P., Song, H., Hwang, J.: Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6, 11782–11792 (2018)CrossRefGoogle Scholar
  5. 5.
    Dong, X., et al.: Fast efficient algorithm for enhancement of low lighting video. In: 2011 IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE (2011)Google Scholar
  6. 6.
    Du, S., Ward, R.: Wavelet-based illumination normalization for face recognition. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, p. II-954. IEEE (2005)Google Scholar
  7. 7.
    Gao, C., Panetta, K., Agaian, S.: No reference color image quality measures. In: 2013 IEEE International Conference on Cybernetics (CYBCO), pp. 243–248. IEEE (2013)Google Scholar
  8. 8.
    Gao, W., et al.: The CAS-PEAL large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 38(1), 149–161 (2008)CrossRefGoogle Scholar
  9. 9.
    García-Montero, M., Redondo-Cabrera, C., López-Sastre, R., Tuytelaars, T.: Fast head pose estimation for human-computer interaction. In: Paredes, R., Cardoso, J.S., Pardo, X.M. (eds.) IbPRIA 2015. LNCS, vol. 9117, pp. 101–110. Springer, Cham (2015). Scholar
  10. 10.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. (6), 643–660 (2001)CrossRefGoogle Scholar
  11. 11.
    Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hassaballah, M., Aly, S.: Face recognition: challenges, achievements and future directions. IET Comput. Vis. 9(4), 614–626 (2015)CrossRefGoogle Scholar
  13. 13.
    Hu, H.: Illumination invariant face recognition based on dual-tree complex wavelet transform. IET Comput. Vis. 9(2), 163–173 (2014)CrossRefGoogle Scholar
  14. 14.
    Iratni, A., Aouache, M., Adel, O.: Adaptive gamma correction-based expert system for nonuniform illumination face enhancement. J. Electron. Imaging 27(2), 023028 (2018)Google Scholar
  15. 15.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  16. 16.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image process. 6(7), 965–976 (1997)CrossRefGoogle Scholar
  17. 17.
    McCool, C., et al.: Bi-modal person recognition on a mobile phone: using mobile phone data. In: 2012 IEEE International Conference on Multimedia and Expo Workshops, pp. 635–640. IEEE (2012)Google Scholar
  18. 18.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar
  19. 19.
    Mustapha, A., Oulefki, A., Bengherabi, M., Boutellaa, E., Algaet, M.A.: Towards nonuniform illumination face enhancement via adaptive contrast stretching. Multimed. Tools Appl. 76(21), 21961–21999 (2017)CrossRefGoogle Scholar
  20. 20.
    Ogden, J.M., Adelson, E.H., Bergen, J.R., Burt, P.J.: Pyramid-based computer graphics. RCA Eng. 30(5), 4–15 (1985)Google Scholar
  21. 21.
    Oulefki, A., Mustapha, A., Boutellaa, E., Bengherabi, M., Tifarine, A.A.: Fuzzy reasoning model to improve face illumination invariance. SIViP 12(3), 421–428 (2018)CrossRefGoogle Scholar
  22. 22.
    Panetta, K., Zhou, Y., Agaian, S., Jia, H.: Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inf. Technol. Biomed. 15(6), 918–928 (2011)CrossRefGoogle Scholar
  23. 23.
    Peli, E.: Contrast in complex images. JOSA A 7(10), 2032–2040 (1990)CrossRefGoogle Scholar
  24. 24.
    Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., Muller, K.E.: Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the First Conference on Visualization in Biomedical Computing, pp. 337–345, May 1990.
  25. 25.
    Pizer, S.M., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)CrossRefGoogle Scholar
  26. 26.
    Poddar, S., Tewary, S., Sharma, D., Karar, V., Ghosh, A., Pal, S.K.: Non-parametric modified histogram equalisation for contrast enhancement. IET Image Proc. 7(7), 641–652 (2013)CrossRefGoogle Scholar
  27. 27.
    Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. Syst. Signal Image Video Technol. 38(1), 35–44 (2004)CrossRefGoogle Scholar
  28. 28.
    Samani, A., Panetta, K., Agaian, S.: Quality assessment of color images affected by transmission error, quantization noise, and noneccentricity pattern noise. In: 2015 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–6. IEEE (2015)Google Scholar
  29. 29.
    Savchenko, A.V.: Deep convolutional neural networks and maximum-likelihood principle in approximate nearest neighbor search. In: Alexandre, L.A., Salvador Sánchez, J., Rodrigues, J.M.F. (eds.) IbPRIA 2017. LNCS, vol. 10255, pp. 42–49. Springer, Cham (2017). Scholar
  30. 30.
    Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4) (2010)CrossRefGoogle Scholar
  31. 31.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 53–58. IEEE (2002)Google Scholar
  32. 32.
    Sun, X., Xu, Q., Zhu, L.: An effective Gaussian fitting approach for image contrast enhancement. IEEE Access (2019)Google Scholar
  33. 33.
    Tan, S.H., Tan, S.B.: The correct interpretation of confidence intervals. Proc. Singapore Healthcare 19(3), 276–278 (2010)CrossRefGoogle Scholar
  34. 34.
    Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Subspace learning from image gradient orientations. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2454–2466 (2012)CrossRefGoogle Scholar
  35. 35.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63(2), 153–161 (2005)CrossRefGoogle Scholar
  36. 36.
    Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electron. 45(1), 68–75 (1999). Scholar
  37. 37.
    Wharton, E., Panetta, K., Agaian, S.: Human visual system based similarity metrics. In: 2008 IEEE International Conference on Systems, Man and Cybernetics, pp. 685–690. IEEE (2008)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre de Développement des Technologies Avancés - CDTAAlgiersAlgeria

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