Abstract
The present paper discusses a method for robust face recognition that works even when only one image is registered and the test image contains a lot of local noises. Two types of facial image decomposition are compared both theoretically and experimentally. That is, we consider both a projectional decomposition, in which images are decomposed into individuality and other components, and a locational decomposition, in which the effects of local noises are suppressed. These two decompositions are simple and powerful and can be applied in collaboration with one another. This collaboration can be realized in a straightforward manner because the decompositions are consistent with one another. They work in a complementary manner and provide better results than when the decompositions are used independently. Finally, we report experimental results obtained using three databases. These results indicate that the combination of projectional and locational decompositions works well, even when only one image is registered and the test images contain significant noise.
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References
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 696–710 (1997)
Black, M., Jepson, A.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 26, 63–84 (1998)
Okabe, T., Sato, Y.: Object recognition based on photometric alignment using ransac. In: Proc. CVPR 2003, vol. 1, pp. 221–228 (2003)
Sakaue, F., Shakunaga, T.: Face recognition by parallel partial projections. In: Proc. ACCV 2004, vol. 1, pp. 1440–1150 (2004)
Batur, A., Hayes III, M.: Linear subspaces for illumination robust face recognition. In: Proc. ICCV 2001, vol. II, pp. 296–301 (2001)
Ohba, K., Ikeuchi, K.: Detectability, uniquensess, and reliability of eigen windows for stable verification of partiallo occluded objects. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 1043–1048 (1997)
Shakunaga, T., Shigenari, K.: Decomposed eigenface for face recognition under various lighting conditions. In: Proc. CVPR 2001, vol. 1, pp. 864–871 (2001)
Wang, H., Li, S., Wang, Y.: Face recognition under varying lighting conditions using self quotient image. In: Proc. IEEE FG 2004, pp. 819–824 (2004)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Generative models for recognition under variable pose and illuminations. In: Proc. FG 2000, pp. 277–284 (2000)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 643–660 (2001)
Martinez, A., Benavente, R.: The AR face database. Technical Report #24, CVC (1998)
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© 2005 Springer-Verlag Berlin Heidelberg
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Sakaue, F., Shakunaga, T. (2005). Combination of Projectional and Locational Decompositions for Robust Face Recognition. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_31
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DOI: https://doi.org/10.1007/11564386_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29229-6
Online ISBN: 978-3-540-32074-6
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