Abstract
In this paper, we introduce an illumination normalization approach within frequency domain by utilizing Discrete Wavelet Transform (DWT) as a transformation function in order to suppress illumination variations and simultaneously amplify facial feature such as eyeball, eyebrow, nose, and mouth. The basic ideas are: 1) transform a face image from spatial domain into frequency domain and then obtain two major components, approximate coefficient (Low frequency) and detail coefficient (High frequency) separately 2) remove total variation in an image by adopting Total Variation Quotient Image (TVQI) or Logarithmic Total Variation (LTV) 3) amplify facial features, which are the significant key for face classification, by adopting Gaussian derivatives and Morphological operators respectively. The efficiency of our proposed approach is evaluated based on a public face database, Yale Face Database B, and its extend version, Extend Yale Face Database B. Our experimental results are demonstrated that the proposed approach archives high recognition rate even though only single image per person was used as the training set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3, 71–86 (1991)
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, 711–720 (1997)
Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: International Conference on Image Processing, vol. 1, p. 129 (1997)
Guo, G., Li, S.Z., Chan, K.: Face recognition by support vector machines. In: IEEE International Conference on Automatic Face and Gesture Recognition, p. 196 (2000)
Wang, H., Li, S.Z., Wang, Y.: Face recognition under varying lighting conditions using self quotient image. In: IEEE International Conference on Automatic Face and Gesture Recognition, p. 819 (2004)
Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Illumination normalization for face recognition and uneven background correction using total variation based image models. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 532–539 (2005)
Wang, J., Wu, L., He, X., Tian, J.: A new method of illumination invariant face recognition. In: International Conference on Innovative Computing, Information and Control, p. 139 (2007)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)
Tao, Q., Veldhuis, R.N.J.: Illumination normalization based on simplified local binary patterns for a face verification system. In: Biometrics Symposium 2007 at The Biometrics Consortium Conference, Baltimore, Maryland, USA, September 2007, pp. 1–7. IEEE Computational Intelligence Society, Los Alamitos (2007)
Choi, S.I., Kim, C., Choi, C.H.: Shadow compensation in 2d images for face recognition. Pattern Recogn. 40, 2118–2125 (2007)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)
Du, S., Ward, R.K.: Wavelet-based illumination normalization for face recognition. In: ICIP, vol. (2), pp. 954–957 (2005)
Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1519–1524 (2006)
Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing 9, 1532–1546 (2000)
Teoh, A.B.J., Goh, Y.Z., Ong, M.G.K.: Illuminated face normalization technique by using wavelet fusion and local binary patterns. In: ICARCV, pp. 422–427. IEEE, Los Alamitos (2008)
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, 643–660 (2001)
Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Transactions on Systems, Man, and Cybernetics, Part B 36, 458–466 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Petpon, A., Srisuk, S. (2010). Illumination Normalization for Robust Face Recognition Using Discrete Wavelet Transform. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-17277-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
eBook Packages: Computer ScienceComputer Science (R0)