Abstract
Pose-invariant face recognition using single frontal training image is considered one of the most difficult challenges in face recognition. To address this problem, we introduce a novel feature extraction method based on learning the manifold of local features. Changes in local features due to pose variations induce a nonlinear manifold in the feature space. Self-organizing map is employed to learn the manifold induced by Gabor filter response of a generic training face database captured at various pose directions. Furthermore, this manifold can be used to represent new face image as a set of points in the feature space. A modular Hausdorff distance measure, which can effectively measure the similarity between two point sets without any correspondence, is also proposed to identify unlabeled subjects. Experimental results on CMU-PIE face database show the effectiveness of the novel method against pose variations.
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References
Li, S., Jain, A.: Handbook of face recognition. Springer, New York (2005)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. Acm Computing Surveys 35(4), 399–459 (2003)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Beymer, D.: Face recognition under varying pose. Technical report. MIT Press, Cambridge (1993)
Beymer, D., Poggio, T.: Face recognition from one example view. In: Proceedings of Fifth International Conference on Computer Vision,1995, pp. 500–507 (1995)
Gross, R., Matthews, I., Baker, S.: Appearance-based face recognition and light-fields. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(4), 449–465 (2004)
Liu, C.J., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. on Image Processing 11(4), 467–476 (2002)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognitionunder variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(3), 411–426 (2007)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1997)
Dubuisson, M., Jain, A.: A modified hausdorff distance for object matching. In: Proc. 12th International Conference on Pattern Recognition, pp. 566–568 (1994)
Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression database. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)
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Aly, S., Tsuruta, N., Taniguchi, Ri. (2009). Self-Organized Gabor Features for Pose Invariant Face Recognition. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_84
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DOI: https://doi.org/10.1007/978-3-642-10677-4_84
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