Abstract
We present a neural system that recognizes faces under strong variations in pose and illumination. The generalization is learnt completely on the basis of examples of a subset of persons (the model database) in frontal and rotated view and under different illuminations. Similarities in identical pose/illumination are calculated by bunch graph matching, identity is coded by similarity rank lists. A neural network based on spike timing decodes these rank lists. We show that identity decisions can be made on the basis of few spikes. Recognition results on a large database of Chinese faces show that the transformations were successfully learnt.
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
Bartlett, M.S., Sejnowski, T.J.: Learning viewpoint-invariant face representations from visual experience in an attractor network. Network – Computation in Neural Systems 9(3), 399–417 (1998)
Földiák, P.: Learning invariance from transformation sequences. Neural Computation 3(2), 194–200 (1991)
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Transactions on Systems, Man, and Cybernetics Part A 38(1), 149–161 (2008)
Gao, W., Cao, B., Shan, S., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale Chinese face database and baseline evaluations. Technical Report JDL-TR-04-FR-001, Joint Research & Development Laboratory for Face Recognition, Chinese Academy of Sciences (2004)
Heinrichs, A., Müller, M.K., Tewes, A.H., Würtz, R.P.: Graphs with principal components of Gabor wavelet features for improved face recognition. In: Cristóbal, G., Javidi, B., Vallmitjana, S. (eds.) Information Optics: 5th International Workshop on Information Optics; WIO 2006, pp. 243–252. American Institute of Physics (2006)
Hinton, G.: Learning translation invariant recognition in massively parallel networks. In: Goos, G., Hartmanis, J. (eds.) PARLE Parallel Architectures and Languages Europe. LNCS, vol. 258, pp. 1–13. Springer, Heidelberg (1987)
Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 42(3), 300–311 (1993)
Lücke, J., Keck, C., von der Malsburg, C.: Rapid convergence to feature layer correspondences. Neural Computation 20(10), 2441–2463 (2008)
Müller, M.K., Heinrichs, A., Tewes, A.H.J., Schäfer, A., Würtz, R.P.: Similarity rank correlation for face recognition under unenrolled pose. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 67–76. Springer, Heidelberg (2007)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 607–626 (2009)
Thorpe, S., Delorme, A., Van Rullen, R.: Spike-based strategies for rapid processing. Neural Networks 14(6-7), 715–725 (2001)
Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
Wiskott, L., Sejnowski, T.J.: Slow feature analysis: Unsupervised learning of invariances. Neural Computation 14(4), 715–770 (2002)
Wiskott, L., von der Malsburg, C.: Recognizing faces by dynamic link matching. Neuroimage 4(3), S14–S18 (1996)
Wolfrum, P., Wolff, C., Lücke, J., von der Malsburg, C.: A recurrent dynamic model for correspondence-based face recognition. Journal of Vision 8(7) (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Müller, M.K., Würtz, R.P. (2009). Learning from Examples to Generalize over Pose and Illumination. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)