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Face Recognition using High-order Local Pattern Descriptor and DCT-based Illuminant Compensation
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  • Journal title : Journal of Broadcast Engineering
  • Volume 21, Issue 1,  2016, pp.51-59
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2016.21.1.51
 Title & Authors
Face Recognition using High-order Local Pattern Descriptor and DCT-based Illuminant Compensation
Choi, Sung-Woo; Kwon, Oh-Seol;
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This paper presents a method of DCT-based illuminant compensation to enhance the accuracy of face recognition under an illuminant change. The basis of the proposed method is that the illuminant is generally located in low-frequency components in the DCT domain. Therefore, the effect of the illuminant can be compensated by controlling the low-frequency components. Moreover, a directional high-order local pattern descriptor is used to detect robust features in the case of face motion. Experiments confirm the performance of the proposed algorithm got up to 95% when tested using a real database.
Face recognition;Discrete Cosine Transform(DCT);Local Pattern Descriptor(LPD);
 Cited by
M. A. Turk and A. P. Pentland, “Eigenfaces for Recognition,” J. Cog. Neurosci., vol. 3, no. 1, pp. 71-86, Mar. 1991. crossref(new window)

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces versus Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997. crossref(new window)

D. Gabor, “Theory of communication,” J.Inst. Elect. Eng., vol. 93, no. 26, pp. 429-457, 1946.

L. Wiskott, J. M. Fellous, N. Kruger, and C. von der Malsbur, “Face recognition by elastic bunch graph matching,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 775-779, Jul. 1997. crossref(new window)

B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local Derivative Pattern Versus Local Binary Pattern: Face Recognition WIth High-Order Local Pattern Descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, Feb. 2010.

W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using Discrete Cosine Transform in logarithm domain,” IEEE Trnas. SMC-B, vol. 36, no. 2, pp. 458-466, Apr. 2006.

S. M. Pizer and E. P. Amburn, “Adaptive histogram equalization and its variations,” Comput. Vis. Graph., Image Process., vol. 39, no. 3, pp. 355-368, 1987. crossref(new window)

J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, and B. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trnas. Medical Imaging, pp. 304-312, Dec. 1988. crossref(new window)

B. S. Min and T. K. Cho, “A Novel Method of Determining Parameters for Contrast Limited Adaptive Histogram Equalization,” Journal of the Korea Academia-Industrial cooperation Society, vol. 14, no. 3, pp. 1378–1387, Jul. 2013. crossref(new window)

Y. Adini, Y. Moses, and S. Ullman, “Face recognition: the problem of compensating for changes in illumination direction,” IEEE Trans Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 721–732, Jul. 1997. crossref(new window)

A. S. Georghiades, P. N. Belhumeur, and D. W. Jacobs, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern, Anal. Mach. Intell., vol. 23, no. 6, pp. 630-660, Jun. 2001. crossref(new window)

V Asha, N. Bhajantri, and P. Nagabhushan. “GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures,” International Journal of Computational Vision and Robotics, vol. 2, no. 4, pp. 302-313, 2011. crossref(new window)

S. Shan, W. Gao, B. Cao, and D. Zhao, "Illumination normalization for robust face recognition against varying lightning conditions," in Proc. IEEE Workshop on AMFG, pp. 157-164, 2003.