Abstract
This paper presents a robust method for recognizing human faces under varying illuminations. Unlike conventional approaches for recognizing faces in the spatial domain, we model the phase information of face images in the frequency domain and use them as features to represent faces. Then, Support Vector Machines (SVM) are applied to claim an identity using different kernel methods. Due to large variations of the face images, algorithms which perform in the space domain need more training images to achieve reasonable performance. On the other hand, the SVM combined with the phase-only representation of faces performs well even with small number of training images. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and 3D Linear Subspace (3DLS) are included in the experiment changing the size of images and the number of training images in order to find the best parameters associated with each method. The illumination subset of the CMU-PIE database is used for the performance evaluation.
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. Journal of Cognitive Neuroscience 3, 72–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. PAMI 19(7), 711–720 (1997)
Penev, P.S.: Local Feature Analysis: A Statistical Theory for Information Representation and Transmission. Ph.D. Thesis, The Rockefeller University (1998)
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. on Neural Networks 13(6), 1450–1464 (2002)
Kong, S.G., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent Advances in Visual and Infrared Face Recognition - A Review. Computer Vision and Image Understanding 97(1), 103–135 (2005)
Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, M.: Face Recognition Vendor Test 2002. Evaluation Report, National Institute of Standards and Technology, 1–56 (2003)
Oppenheim, A.V., Lim, J.S.: The Importance of phase in signals. Proc. IEEE 69(5), 529–541 (1981)
Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.K.: Eigenphases vs. Eigenfaces. In: Proceeding of the ICPR (2004)
Belhumeur, P., Kriegman, D.: What is the Set of Images of an Object under All Possible Illumination Conditions. Int. J. Computer Vision 28(3), 245–260 (1998)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Schölkopf, B.: Support Vector Learning. Oldenbourg-Verlag, Germany (1997)
Phillips, P.J.: Support vector machines applied to face recognition. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems 11 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heo, J., Savvides, M., Vijayakumar, B.V.K. (2005). Illumination Tolerant Face Recognition Using Phase-Only Support Vector Machines in the Frequency Domain. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_8
Download citation
DOI: https://doi.org/10.1007/11552499_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
eBook Packages: Computer ScienceComputer Science (R0)