Abstract
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.
Similar content being viewed by others
References
Chen GY, Bui TD, Krzyzak A (2009) Invariant pattern recognition using radon, dual-tree complex wavelet and Fourier transforms. Pattern Recogn 42(9):2013–2019
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):458–466
Chen GY, Xie WF (2011) Wavelet-based moment invariants for pattern recognition. Opt Eng 50(7):077205
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, China
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: https://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/diffgaussians/index.html
Dewantara BSB, Miura J (2016) OptiFuzz: a robust illumination invariant face recognition system and its implementation. Mach Vis Appl 27:877–891
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):1165–1175
Faraji MR, Qi J (2015) Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns. IET Comput Vis 9(3):390–399
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906
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. 10–18
Han H, Shan S, Chen X, Gao W (2013) A comparative study on illumination preprocessing in face recognition. Pattern Recogn 46(6):1691–1699
Heusch G, Cardinaux F, Marcel S (2005) Lighting normalization algorithms for face verification,” IDIAP-com 05–03
Hu H (2015) Illumination invariant face recognition based on dual-tree complex wavelet transform. IET Comput Vis 9(2):163–173
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):897–1056
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):1735–1747
Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698
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):2543–2554
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. 467–471
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. 1617–1623
Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115
Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune Teller: Predicting Your Career Path. Thirtieth AAAI Conference on Artificial Intelligence, pp. 201–207
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):12–21
Oppenheim AV, Schafer RW, Stockham TG (1968) Nonlinear filtering of multiplied and convolved signals. Proc IEEE 56(8):1264–1291
Park YK, Park SL, Kim JK (2008) Retinex method based on adaptive smoothing for illumination invariant face recognition. Signal Process 88(8):1929–1945
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. 1–4
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)
Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618
Struc V, Pavesic N (2009) Illumination invariant face recognition by non-local smoothing. Proceedings of BIOID MultiComm, LNCS 5707, Springer, pp. 1–8
Tan X, Triggs B (2010) Enhanced local texture sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
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. 1397–1400
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):462–465
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):1807–1821
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):252–258
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):2599–2606
Zou X, Kittler J, Messer K (2007) Illumination invariant face recognition: a survey. In: Proceedings of the Biometrics: Theory, Applications, and Systems, pp. 1–8
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that there is no conflict of interests for this paper.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chen, G.Y. An experimental study for the effects of noise on face recognition algorithms under varying illumination. Multimed Tools Appl 78, 26615–26631 (2019). https://doi.org/10.1007/s11042-019-07810-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-07810-y