The discrete cosine transform (DCT) plus local normalization: a novel two-stage method for de-illumination in face recognition

• Autorzy: Zhao M. , Wang Y. , Shi Z. , Zhang J.
• Języki publikacji: EN

Abstrakty

EN

To deal with illumination variations in face recognition, a novel two-stage illumination normalization method is proposed in this paper. Firstly, a discrete cosine transform (DCT) is used on the original images in logarithm domain. DC coefficient is set based on the average pixel value of all the within-class training samples and some low frequency AC coefficients are set to zero to eliminate illumination variations in large areas. Secondly, local normalization method, which can minimize illumination variations in small areas, is used on the inverse DCT images. This makes the pixel values on the processed images be close to or equal to that of the normal illumination condition. Experimental results, both on Yale B database and Extended Yale B database, show that the proposed method can eliminate effect of illumination variations effectively and improve performance of face recognition methods significantly. The present method does not demand modeling step and can eliminate the effect of illumination variations before face recognition. In this way, it can be used as a preprocessing step for any existing face recognition method.

Słowa kluczowe

EN

face recognition; discrete cosine transform (DCT); local normalization method; de-illumination; retinex theory

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2011
• Tom: Vol. 41, nr 4
• Strony: 825--839
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Zhao M.

autor

Wang Y.

autor

Shi Z.

autor

Zhang J.

• School of Computer Science and Engineering, Xi'an University of Technology, Xi'an710048, China

Bibliografia

• [1] CHELLAPPA R., WILSON C.L., SIROHEY S., Human and machine recognition of faces: A survey, Proceedings of the IEEE 83(5),1995, pp. 705–741.
• [2] JIALI ZHAO, YONG SU, DEJUN WANG, SIWEI LUO, Illumination ratio image: Synthesizing and recognition with varying illuminations, Pattern Recognition Letters 24(15), 2003, pp. 2703–2710.
• [3] TURK M., PENTLAND A., Eigenfaces for recognition, Journal of Cognitive Neuroscience 3(1), 1991, pp. 71–86.
• [4] BARTLETT M.S, MOVELLAN J.R., SEJNOWSKI T.J., Face recognition by independent component analysis, IEEE Transactions on Neural Networks 13(6), 2002, pp. 1450–1464.
• [5] ADINI Y., MOSES Y., ULLMAN S., Face recognition: The problem of compensating for changes in illumination direction, IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 1997, pp. 721–732.
• [6] BELHUMEUR P.N., HESPANHA J.P., KRIEGMAN D.J., Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 1997, pp. 711–720.
• [7] CHEN H.F., BELHUMEUR P.N., JACOBS, D.W. , In search of illumination invariants, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, 2000, pp. 254–261.
• [8] SHASHUA A., RIKLIN-RAVIV T., The quotient image: Class-based re-rendering and recognition with varying illuminations, IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 2001, pp. 129–139.
• [9] HALLINAN P.W., A low-dimensional representation of human faces for arbitrary lighting conditions, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR’94, 1994, pp. 995–999 .
• [10] BICHSEL M., Illumination invariant object recognition, Proceedings of International Conference on Image Processing, Vol. 3, 1995, pp. 620–623.
• [11] BELHUMEUR P.N., KRIEGMAN D.J., What is the set of images of an object under all possible illumination conditions, International Journal of Computer Vision 28(3), 1998, pp. 245–260.
• [12] 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 Transactions on Pattern Analysis and Machine Intelligence 23(6), 2001, pp. 643–660.
• [13] BASRI R., JACOBS D.W., Lambertian reflectance and linear subspaces, IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 2003, pp. 218–233.
• [14] PIZER S.M., AMBURN E.P., AUSTIN J.D., CROMARTIE R., GESELOWITZ A., GREER T., TER HAAR ROMENY B., ZIMMERMAN J.B., ZUIDERVELD K., Adaptive histogram equalization and its variations, Computer Vision, Graphics, and Image Processing 39(3), 1987, pp. 355–368.
• [15] KLEIHORST R.P., LAGENDIIK R.L., BIEMOND J., An adaptive order-statistic noise filter for gamma-corrected image sequences, IEEE Transactions on Image Processing 6(10), 1997, pp. 1442–1446.
• [16] FARID H., Blind inverse gamma correction, IEEE Transactions on Image Processing 10(10), 2001, pp. 1428–1433.
• [17] WEN YI ZHAO, CHELLAPPA R., SFS based view synthesis for robust face recognition, Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 285–292.
• [18] SHIGUANG SHAN, WEN GAO, BO CAO, DEBIN ZHAO, Illumination normalization for robust face recognition against varying lighting conditions, Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003, pp. 157–164.
• [19] XUDONG XIE, KIN-MAN LAM, An efficient illumination normalization method for face recognition, Pattern Recognition Letters 27(6), 2006, pp. 609–617.
• [20] RAO K.R., YIP P., Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press, Boston, 1990.
• [21] LAND E.H., MCCANN J.J., Lightness and retinex theory, Journal of the Optical Society of America 61(1), 1971, pp. 1–11.
• [22] YOUNG KYUNG PARK, SEOK LAI PARK, JOONG KYU KIM, Retinex method based on adaptive smoothing for illumination invariant face recognition, Signal Processing 88(8), 2008, pp. 1929–1945.
• [23] JOBSON D.J., RAHMAN Z., WOODELL G.A., Properties and performance of a center/surround retinex, IEEE Transactions on Image Processing 6(3), 1997, pp. 451–462.
• [24] CHEN W., MENG JOO ER, SHIQIAN WU, Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(2), 2006, pp. 458–466.
• [25] ZHAO W., CHELLAPPA R., PHILLIPS P.J., ROSENFELD A., Face recognition: A literature survey, ACM Computing Surveys 35(4), 2003, pp. 399–458.
• [26] LIE ZHANG, SAMARAS D., Face recognition under variable lighting using harmonic image exemplars, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 2003, pp. I-19–I-25.
• [27] LEE K.C., HO J., KRIEGMAN D. J., Acquiring linear subspaces for face recognition under variable lighting, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 2005, pp. 684–698.