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An adaptive illumination normalization using non-linear regression for robust person identification under varying illuminations

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  • P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, 1997. doi: 10.1109/34.598228
  • M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, 1991, pp. 586–591. doi: 10.1109/CVPR.1991.139758
  • M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1. pp. 71–86, 1991. doi: 10.1162/jocn.1991.3.1.71
  • D. Toth, T. Aach, and V. Metzler, “Illumination-invariant change detection,” in Image Analysis and Interpretation, 2000. Proceedings. 4th IEEE Southwest Symposium, 2000, pp. 3–7.
  • M. Savvides and B. V. K. Kumar, “Illumination normalization using logarithm transforms for face authentication,” in Audio-and VideoBased Biometric Person Authentication, 2003, pp. 549–556. doi: 10.1007/3-540-44887-X_65
  • W. Hwang, H. Wang, H. Kim, S.-C. Kee, and J. Kim, “Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation,” IEEE Trans. image Process., vol. 20, no. 4, pp. 1152–1165, 2010. doi: 10.1109/TIP.2010.2083674
  • S.-M. Huang and J.-F. Yang, “Improved principal component regression for face recognition under illumination variations,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 179–182, 2012. doi: 10.1109/LSP.2012.2185492
  • M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Robust face recognition for uncontrolled pose and illumination changes,” ieee Trans. Syst. man, Cybern. Syst., vol. 43, no. 1, pp. 149–163, 2013.
  • V. P. Vishwakarma, “Illumination normalization using fuzzy filter in DCT domain for face recognition,” Int. J. Mach. Learn. Cybern., vol. 6, no. 1, pp. 17–34, 2015. doi: 10.1007/s13042-013-0182-4
  • H. Roy and D. Bhattacharjee, “Local-gravity-face (LG-face) for illumination-invariant and heterogeneous face recognition,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 7, pp. 1412–1424, 2016. doi: 10.1109/TIFS.2016.2530043
  • Y. Cheng, L. Jiao, Y. Tong, Z. Li, Y. Hu, and X. Cao, “Directional illumination estimation sets and multilevel matching metric for illumination-robust face recognition,” IEEE Access, vol. 5, pp. 25835–25845, 2017. doi: 10.1109/ACCESS.2017.2766128
  • J. Yadav, N. Rajpal, and R. Mehta, “An improved hybrid illumination normalisation and feature extraction model for face recognition,” Int. J. Appl. Pattern Recognit., vol. 5, no. 2, pp. 149–170, 2018. doi: 10.1504/IJAPR.2018.092523
  • J. Yadav, N. Rajpal, and R. Mehta, “A new illumination normalization framework via homomorphic filtering and reflectance ratio in DWT domain for face recognition,” J. Intell. Fuzzy Syst., vol. 35, no. 5, pp. 5265–5277, 2018. doi: 10.3233/JIFS-169810
  • V. P. Vishwakarma and T. Goel, “An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition,” Multimed. Tools Appl., vol. 78, no. 11, pp. 15213–15233, 2019. doi: 10.1007/s11042-018-6837-0
  • W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,” IEEE Trans. Syst. Man, Cybern. Part B, vol. 36, no. 2, pp. 458–466, 2006. doi: 10.1109/TSMCB.2005.857353
  • P.-H. Lee, S.-W. Wu, and Y.-P. Hung, “Illumination compensation using oriented local histogram equalization and its application to face recognition,” IEEE Trans. Image Process., vol. 21, no. 9, pp. 4280–4289, 2012. doi: 10.1109/TIP.2012.2202670
  • M. R. Faraji and X. Qi, “Face recognition under varying illumination with logarithmic fractal analysis,” IEEE Signal Process. Lett., vol. 21, no. 12, pp. 1457–1461, 2014. doi: 10.1109/LSP.2014.2343213
  • Y. Hui-xian and C. Yong-yong, “Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination,” IET Biometrics, vol. 5, no. 2, pp. 76–82, 2016.
  • K.-C. Lee, J. Ho, and D. J. Kriegman, “Acquiring linear subspaces for face recognition under variable lighting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 5, pp. 684–698, 2005. doi: 10.1109/TPAMI.2005.92
  • X. Xie, W.-S. Zheng, J. Lai, P. C. Yuen, and C. Y. Suen, “Normalization of face illumination based on large-and small-scale features,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1807–1821, 2011. doi: 10.1109/TIP.2010.2097270
  • T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression (PIE) database,” in Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, 2002, pp. 53– 58.
  • A. Georghiades, “Yale Face Database,” Center for Computational Vision and Control at Yale University, 1997. [Online]. Available: http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
  • “YALE B Face Database,” 2001. [Online]. Available: http://vision.ucsd.edu/~iskwak/ExtYaleDatabase/Yale Face Database.htm.
  • K. C. Lee, J. Ho, and D. Kriegman, “ExtendedYaleB ‘Acquiring Linear Subspaces for Face Recognition under Variable Lighting,’” IEEE Trans. Pattern Anal. Mach. Intelligence, vol. 27, no. 5. pp. 684–698, 2005. doi: 10.1109/TPAMI.2005.92
  • A. R. Martinez and R. Benavente, “The AR face database,” Comput. Vis. Center, Tech. Report24, vol. 3, p. 5, 1998.
  • V. P. Vishwakarma, S. Pandey, and M. N. Gupta, “Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization,” Int. J. Recent Trends Eng., vol. 1, no. 1, pp. 318–322, 2009.
  • R. Gonzalez and R. Woods, Digital image processing. India: Pearson Education, 2006.
  • G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489– 501, 2006. doi: 10.1016/j.neucom.2005.12.126
  • G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Trans. Syst. Man, Cybern. Part B, vol. 42, no. 2, pp. 513–529, 2012. doi: 10.1109/TSMCB.2011.2168604
  • C. M. Wong, C. M. Vong, P. K. Wong, and J. Cao, “Kernel-based multilayer extreme learning machines for representation learning,” IEEE Trans. neural networks Learn. Syst., vol. 29, no. 3, pp. 757–762, 2016. doi: 10.1109/TNNLS.2016.2636834
  • V. P. Vishwakarma, “Deterministic learning machine for face recognition with multi-model feature extraction,” in Contemporary Computing (IC3), 2016 Ninth International Conference on, 2016, pp. 1–6.

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