Abstract
Face recognition systems have to deal with the problem that not all variations of all persons can be enrolled. Rather, the variations of most persons must be modeled. Explicit modeling of different poses is awkward and time consuming. Here, we present a subsystem that builds a model of pose variation by keeping a model database of persons in both poses, additionally to the gallery of clients known in only one pose. An identification or verification decision for probe images is made on the basis of the rank order of similarities with the model database. Identification achieves up to 100% recognition rate on 300 pairs of testing images with 45 degrees pose variation within the CAS-PEAL database, the equal error rate for verification reaches 0.5%.
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Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)
Saul, L.K., Roweis, S.T.: Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research 4, 119–155 (2003)
Okada, K., von der Malsburg, C.: Pose-invariant face recognition with parametric linear subspaces. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, Washington, DC, pp. 71–76 (2002)
Tewes, A.: A Flexible Object Model for Encoding and Matching Human Faces. Shaker Verlag, Ithaca, NY, USA (2006)
Tewes, A., Würtz, R.P., von der Malsburg, C.: A flexible object model for recognising and synthesising facial expressions. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 81–90. Springer, Heidelberg (2005)
Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C — The Art of Scientific Programmming. Cambridge University Press, Cambridge (1988)
Andrew, L., Rukhin, A.O.: Nonparametric measures of dependence for biometric data studies. Journal of Statistical Planning and Inference 131, 1–18 (2005)
Ayinde, O., Yang, Y.H.: Face recognition approach based on rank correlation of gabor-filtered images. Pattern Recognition 35(6), 1275–1289 (2002)
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)
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)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances NIPS 7, pp. 625–632. MIT Press, Cambridge, MA (1995)
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.) WIO 2006. Information Optics: 5th International Workshop on Information Optics, American Institute of Physics, pp. 243–252 (2006)
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)
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Müller, M.K., Heinrichs, A., Tewes, A.H.J., Schäfer, A., Würtz, R.P. (2007). Similarity Rank Correlation for Face Recognition Under Unenrolled Pose. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_8
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DOI: https://doi.org/10.1007/978-3-540-74549-5_8
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