Abstract
Most face based biometric systems and the underlying recognition algorithms are often more suited for verification (one-to-one comparison) instead of identification (one-to-many comparison) purposes. This is even more true in case of large face database, as the computational cost of an accurate comparison between the query and a gallery of many thousands of individuals could be too high for practical applications. In this paper we present a 3D based face recognition method which relies on normal image to represent and compare face geometry. It features fast comparison time and good robustness to a wide range of expressive variations thanks to an expression weighting mask, automatically generated for each enrolled subject. To better address one-to-many recognition applications, the proposed approach is improved via DFT based indexing of face descriptors and k-d-tree based spatial access to clusters of similar faces. We include experimental results showing the effectiveness of the presented method in terms of recognition accuracy and the improvements in one-to-many recognition time achieved thanks to indexing and retrieval techniques applied to a large parametric 3D face database.
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References
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)
Perronnin, G., Dugelay, J.L.: An Introduction to biometrics and face recognition. In: Proc. of IMAGE 2003: Learning, Understanding, Information Retrieval, Medical, Cagliari, Italy (June 2003)
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural networks 13(6), 1450–1464 (2002)
Zhang, J., Yan, Y., Lades, M.: Face Recognition: Eigenface, Elastic Matching, and Neural Nets. Proc. of the IEEE 85(9), 1423–1435 (1997)
Tan, T., Yan, H.: Face recognition by fractal transformations. In: Proc. of 1999 IEEE Int. Conference on Acoustics, Speech, and Signal Processing, March 1999, vol. 6(6), pp. 3537–3540 (1999)
Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, M.: Face Recognition Vendor Test: Evaluation Report (March 2003), http://www.frvt.org
Achermann, B., Jiang, X., Bunke, H.: Face recognition using range images. In: Proc. of International Conference on Virtual Systems and MultiMedia, pp. 129–136 (1997)
Achermann, B., Bunke, H.: Classifying range images of human faces with the hausdorff distance. In: Proc. of 15th International Conference on Pattern Recognition, Barcelona, Spain, vol. 2, pp. 813–817 (2000)
Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based face surface recognition using spherical correlation principal directions for curved object recognition. In: Proc. of Third International Conference on Automated Face and Gesture Recognition, pp. 372–377 (1998)
Hesher, C., Srivastava, A., Erlebacher, G.: A novel technique for face recognition using range images. In: Proc. of Seventh Int’l. Symp. on Signal Processing and Its Applications, Paris, France (July 2003)
Medioni, G., Waupotitsch, R.: Face recognition and modeling in 3D. In: Proc. of IEEE Int’l. Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2003), October 2003, pp. 232–233 (2003)
Gu, X., Gortler, S., Hoppe, H.: Geometry images. In: Proc. of SIGGRAPH 2002, San Antonio, Texas, pp. 355–361. ACM, New York (2002)
Lee, W., Magnenat-Thalmann, N.: Head Modeling from Pictures and Morphing in 3D with Image Metamorphosis based on triangulation. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) CAPTECH 1998. LNCS (LNAI), vol. 1537, pp. 254–267. Springer, Heidelberg (1998)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730. Springer, Heidelberg (1993)
Petrakis, E.G.M., Faloutsos, C.: ImageMap: An Image Indexing Method Based on Spatial Similarity. Proc. of IEEE Transactions on Knowledge and Data Engineering 14(15), 979–987 (1999)
Berchtold, S., Keim, D.A., Kriegel, H.-P.: The X-Tree: An Index Structure for High-Dimensional Data. In: Proc. of 22nd Very Large Data Base Conf., pp. 28–39 (1996)
Katayama, N., Satoh, S.: The SR-tree: An Index Structure for High-Dimensional Nearest Neightbor Queries. In: Proc. of ACM SIGMOD, pp. 269–380 (1997)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Comm. ACM 18(9), 509–517 (1975)
Phillips, J.P., Moon, H., Rizvi, A.S., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Mathematical Software 3(3), 209–226 (1997)
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Abate, A.F., Nappi, M., Ricciardi, S., Sabatino, G. (2005). One to Many 3D Face Recognition Enhanced Through k-d-Tree Based Spatial Access. In: Candan, K.S., Celentano, A. (eds) Advances in Multimedia Information Systems. MIS 2005. Lecture Notes in Computer Science, vol 3665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551898_4
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DOI: https://doi.org/10.1007/11551898_4
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