Illumination normalization using independent component analysis and filtering

References

• Han H, Shan S, Chen X, et al. A comparative study on illumination pre-processing in face recognition. Pattern Recogn. 2013;46:1691–1699. doi: 10.1016/j.patcog.2012.11.022
   Web of Science ®Google Scholar
• Ochoa-Villegas MA, Nolazco-Flores JA, Barron-Cano O, et al. Addressing the illumination challenge in two-dimensional face recognition: a survey. IET Comput Vis. 2015;9(6):978–992. doi: 10.1049/iet-cvi.2014.0086
   Web of Science ®Google Scholar
• Choi S. Combined features for face recognition under illumination variation. IEEE Electron Lett. 2016;52(1):31–33. doi: 10.1049/el.2015.2936
   Web of Science ®Google Scholar
• Chen C, Shen H. Illumination compensation method using edge-weakening guided image filter for face recognition. IEEE Electron Lett. 2015;51(19):1495–1497. doi: 10.1049/el.2015.1667
   Web of Science ®Google Scholar
• Khan A, Rinner B, Cavallaro A. Observing moving targets by cooperative mobile robots: a review. IEEE Trans Cybern. 2017, in press.
   Google Scholar
• Faraji MR, Qi X. Face recognition under varying illuminations using logarithmic fractal dimension-based complete eight local directional patterns. Neurocomputing. 2016;199:16–30. doi: 10.1016/j.neucom.2016.01.094
   Web of Science ®Google Scholar
• Lee K, Ho J, Kriegman D. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell. 2005;27(5):684–698. doi: 10.1109/TPAMI.2005.92
   PubMed Web of Science ®Google Scholar
• Adini Y, Moses Y, Ullman S. Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans Pattern Anal Mach Intell. 1997;19(7):721–732. doi: 10.1109/34.598229
   Web of Science ®Google Scholar
• Roy H, Bhattacharjee D. Local-gravity-face (LG-face) for illumination-invariant and heterogeneous face recognition. IEEE Trans Inf Forensic Sec. 2016;11(7):1412–1424. doi: 10.1109/TIFS.2016.2530043
   Web of Science ®Google Scholar
• Zou X, Kittler J, Messer K. Illumination invariant face recognition: a survey. Proc of the Biometrics: Theory, Applications, and Systems; Washington (DC); 2007. p. 1–8.
   Google Scholar
• Lai Z, Dai D, Ren C, et al. Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans Image Process. 2015;24(6):1735–1747. doi: 10.1109/TIP.2015.2409988
   PubMed Web of Science ®Google Scholar
• Wang H, Li SZ, Wang Y. Face recognition under varying lighting conditions using self-quotient image. Proc of the Automatic Face and Gesture Recognition; Seoul; 2004. p. 819–824.
   Google Scholar
• Chen T, Yin W, Zhou XS, et al. Total variation models for variable lighting face recognition. IEEE Trans Pattern Anal Mach Intell. 2006;28:1519–1524. doi: 10.1109/TPAMI.2006.195
   PubMed Web of Science ®Google Scholar
• Tan X, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process. 2010;19:1635–1650. doi: 10.1109/TIP.2009.2033625
   PubMed Web of Science ®Google Scholar
• Wang B, Li W, Yang W, et al. Illumination normalization based on Weber’s law with application to face recognition. IEEE Signal Proc Let. 2011;18:462–465. doi: 10.1109/LSP.2011.2158998
   Web of Science ®Google Scholar
• Wu Y, Jiang Y, Zhou Y, et al. Generalized Weber-face for illumination-robust face recognition. Neurocomputing. 2014;136:262–267. doi: 10.1016/j.neucom.2014.01.006
   Web of Science ®Google Scholar
• McLaughlin N, Ming J, Crookes D. Largest matching areas for illumination and occlusion robust face recognition. IEEE Trans Cybern. 2017;47(3):796–808. doi: 10.1109/TCYB.2016.2529300
   Web of Science ®Google Scholar
• Lee S, Lee C. Multiscale morphology based illumination normalization with enhanced local textures for face recognition. Expert Syst Appl. 2016;62:347–357. doi: 10.1016/j.eswa.2016.06.039
   Web of Science ®Google Scholar
• Land EH, McCann JJ. Lightness and retinex theory. J Opt Soc Am. 1971;61(1):1–11. doi: 10.1364/JOSA.61.000001
   PubMed Web of Science ®Google Scholar
• Stainvas I, Lowe D. A generative model for separating illumination and reflectance from images. J Mach Learn Res. 2003;4:1499–1519.
   Web of Science ®Google Scholar
• Hyvärinen A, Oja E. Independent component analysis: algorithms and application. Neural Netw. 2000;13(4–5):411–430. doi: 10.1016/S0893-6080(00)00026-5
   PubMed Web of Science ®Google Scholar
• Khan A, Kim I. Sparse kernel independent component analysis for blind source separation. J Opt Soc Korea. 2008;12(3):121–125. doi: 10.3807/JOSK.2008.12.3.121
   Google Scholar
• Khan A, Kim I. Sparse independent component analysis with interpolation for blind source separation. Proc 2nd IEEE Int Conf on Computer, Control & Communication; Karachi; 2009.
   Google Scholar
• Available from: https://research.ics.aalto.fi/ica/fastica/index.shtml.
   Google Scholar
• Hyvarinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput. 1997;9(7):1483–1492. doi: 10.1162/neco.1997.9.7.1483
   Web of Science ®Google Scholar
• Tomasi C, Manduchi R. Bilateral filtering for gray and colour images. Proc of IEEE Conf on Comput Vis; Durham; 1998. p. 839–846.
   Google Scholar
• Turk M, Pentland A. Face recognition using Eigen faces. Proc of IEEE Conf on Comput Vis and Pattern Recogn; Lahaina; 1991. p. 586–591.
   Google Scholar
• Bartlett MS, Movellan JR, Sejnowski TJ. Face recognition by independent component analysis. IEEE Trans Neural Netw. 2002;13(6):1450–1464. doi: 10.1109/TNN.2002.804287
   PubMed Web of Science ®Google Scholar
• Liu C, Wechsler H. Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process. 2002;11(4):467–476. doi: 10.1109/TIP.2002.999679
   PubMed Web of Science ®Google Scholar