Abstract
In this work we propose a new method for face recognition that successfully handles occluded faces. We propose an innovative improvement that allows to detect and discard occluded zones of the face, thus making recognition more robust in the presence of occlusion. We provide experimental results that show that the proposed method performs well in practice.
Chapter PDF
Similar content being viewed by others
References
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 681–685 (2001)
Stegmann, M.B., Ersbøll, B.K., Larsen, R.: FAME - A Flexible Appearance Modelling Environment. IEEE Transactions on Medical Imaging 22(10), 1319–1331 (2003)
Kahraman, F., Kurt, B., Gokmen, M.: Robust face alignment for illumination and pose invariant face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)
Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Candès, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59(8), 1207–1223 (2006)
Candès, E.J., Tao, T.: Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory 52(12), 5406–5425 (2006)
Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)
Lin, D., Tang, X.: Quality-driven face occlusion detection and recovery. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)
Zhou, Z., Wagner, A., Mobahi, H., Wright, J., Ma, Y.: Face recognition with contiguous occlusion using markov random fields. In: International Conference on Computer Vision, ICCV (2009)
Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 218–233 (2003)
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), 684–698 (2005)
Donoho, D.L.: For most large underdetermined systems of linear equations the minimal, L1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics 59, 797–829 (2004)
Martinez, A.M., Benavente, R.: The AR face database. Technical Report 24, CVC (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Morelli Andres, A., Padovani, S., Tepper, M., Mejail, M., Jacobo, J. (2012). Randomized Face Recognition on Partially Occluded Images. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-33275-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33274-6
Online ISBN: 978-3-642-33275-3
eBook Packages: Computer ScienceComputer Science (R0)