Abstract
In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers.
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References
3DFace (2009). 3D face project web page. http://www.3dface.org/home/welcome.html.
Achermann, B., Jiang, X., & Bunke, H. (1997). Face recognition using range data. In Proceedings of the international conference on virtual systems and multimedia (pp. 129–136). Geneva: IEEE Press.
Al-Osaimi, F., Bennamoun, M., & Mian, A. (2009). An expression deformation approach to non-rigid 3D face recognition. International Journal of Computer Vision, 81(3), 302–316. doi:10.1007/s11263-008-0174-0.
Alyüz, N., Gökberk, B., & Akarun, L. (2009). Regional registration and curvature descriptors for expression resistant 3D face recognition. In Proceedings of the IEEE 17th signal processing and communications applications conference (SIU-2009) (pp. 544–547).
Bazen, A. M., & Veldhuis, R. N. J. (2004). Likelihood ratio-based biometric verification. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 86–94.
Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256. doi:10.1109/34.121791.
Beumer, G. M., Tao, Q., Bazen, AM, & Veldhuis, R. N. J. (2006). A landmark paper in face recognition. In Proceedings of the 7th international conference on automatic face and gesture recognition (FGR 2006) (pp. 73–78). Los Alamitos: IEEE Computer Society Press.
Boehnen, C., Peters, T., & Flynn, P. J. (2009). 3d signatures for fast 3D face recognition. In Proceedings of the third international conference on advances in biometrics (ICB ’09) (pp. 12–21). Berlin: Springer.
Boom, B. J., Beumer, G. M., Spreeuwers, L. J., & Veldhuis, R. N. J. (2006). The effect of image resolution on the performance of a face recognition system. In Proceedings of the 9th international conference on control, automation, robotics and vision (ICARCV), Singapore, Malaysia (pp. 409–414).
Boom, B. J., Spreeuwers, L. J., & Veldhuis, R. N. J. (2007). Automatic face alignment by maximizing similarity score. In A. Fred & A. K. Jain (Eds.), Proceedings of the 7th international workshop on pattern recognition in information systems (Madeira, Portugal, 2007) (pp. 221–230). Madeira: INSTICC Press. Biosignals.
Bowyer, K. W., Chang, K., & Flynn, P. (2006). A survey of approaches and challenges in 3D and multi-modal 3d+2d face recognition. Computer Vision and Image Understanding, 101(1), 1–15. doi:10.1016/j.cviu.2005.05.005.
Brent, R. P. (1973). Algorithms for minimization without derivatives. Englewood Cliffs: Prentice Hall, Chap. 5.
Buhan, I. R., Doumen, J. M., Hartel, P. H., Tang, Q., & Veldhuis, R. N. J. (2010). Embedding renewable cryptographic keys into continuous noisy data. International Journal of Information Security, 9(3), 193–208.
Cartoux, J. Y., Lapreste, J. T., & Richetin, M. (1989). Face authentification or recognition by profile extraction from range images. In Proceedings of the workshop on interpretation of 3D scenes (pp. 194–199).
Chen, C., Veldhuis, R. N. J., Kevenaar, TAM, & Akkermans, A. H. M. (2009). Biometric quantization through detection rate optimized bit allocation. EURASIP Journal on Advances in Signal Processing, 2009, 784–834.
Colombo, A., Cusano, C., & Schettini, R. (2006). Detection and restoration of occlusions for 3D face recognition. In IEEE international conference on multimedia and expo (pp. 1541–1544). doi:10.1109/ICME.2006.262837.
Faltemier, T., Bowyer, K., & Flynn, P. (2008a). A region ensemble for 3-d face recognition. IEEE Transactions on Information Forensics and Security, 3(1), 62–73. doi:10.1109/TIFS.2007.916287.
Faltemier, T. C., Bowyer, K. W., & Flynn, P. J. (2008b). Using multi-instance enrollment to improve performance of 3D face recognition. Computer Vision and Image Understanding, 112(2), 114–125. doi:10.1016/j.cviu.2008.01.004.
Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. http://doi.acm.org/10.1145/358669.358692.
Gokberk, B., Irfanoglu, M. O., & Akarun, L. (2006). 3D shape-based face representation and feature extraction for face recognition. Image and Vision Computing, 24(8), 857–869. doi:10.1016/j.imavis.2006.02.009.
Gonzalez-Rodriguez, J., Fiérrez-Aguilar, J., Ortega-Garcia, J., & Lucena-Molina, J. J. (2002). Biometric identification in forensic cases according to the bayesian approach. In Proceedings of the international ECCV 2002 workshop Copenhagen on biometric authentication (pp. 177–185). London: Springer.
van der Heijden, F., & Spreeuwers, L. J. (2007). Image processing. In Multimedia retrieval (pp. 163–165). Berlin: Springer.
Hesher, C., Srivastava, A., & Erlebacher, G. (2003). A novel technique for face recognition using range imaging. In 7th international symposium on signal processing and its applications (Vol. 2, pp. 201–204). doi:10.1109/ISSPA.2003.1224850.
Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37. doi:10.1109/34.824819.
Kakadiaris, I. A., Passalis, G., Toderici, G., Murtuza, M. N., Lu, Y., Karampatziakis, N., & Theoharis, T. (2007). Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 640–649. doi:10.1109/TPAMI.2007.1017.
Kelkboom, E. J. C., Zhou, X., Breebaart, J., Veldhuis, R. N. J., & Busch, C. (2009). Multi-algorithm fusion with template protection. In IEEE 3rd international conference on biometrics: theory, applications, and systems, 2009 (BTAS ’09) (pp. 1–8). Washington: IEEE Computer Society Press.
Kelkboom, E. J. C., Garcia-Molina, G., Breebaart, J., Veldhuis, R. N. J., Kevenaar, T. A. M., & Jonker, W. (2010). Binary biometrics: an analytic framework to estimate the performance curves under gaussian assumption. IEEE Transactions on Systems, Man and Cybernetics. Part A. Systems and Humans, 40(3), 555–571.
Kuncheva, L. I., Whitaker, C. J., & Duin, R. P. W. (2003). Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications, 6, 22–31.
Maurer, T., Guigonis, D., Maslov, I., Pesenti, B., Tsaregorodtsev, A., West, D., & Medioni, G. (2005). Performance of geometrix activeid™ 3d face recognition engine on the frgc data. In Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05)—workshops (p. 154). Washington: IEEE Computer Society. doi:10.1109/CVPR.2005.581.
Medioni, G., & Waupotitsch, R. (2003). Face modeling and recognition in 3-d. In Proceedings of the IEEE international workshop on analysis and modeling of faces and gestures (AMFG ’03) (p. 232). Washington: IEEE Computer Society.
Mian, A., Bennamoun, M., & Owens, R. (2007). An efficient multimodal 2D–3D hybrid approach to automatic face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(11), 1927–1943. doi:10.1109/TPAMI.2007.1105.
Papatheodorou, T., & Rueckert, D. (2005). Evaluation of 3D face recognition using registration and pca. In T. Kanade, A. K. Jain, & N. K. Ratha (Eds.), Lecture notes in computer science: Vol. 3546. Proceedings of the 5th international conference on audio- and video-based biometric person authentication (AVBPA 2005) (pp. 997–1009). Hilton Rye Town: Springer.
Papatheodorou, T., & Rueckert, D. (2007). 3D face recognition. Vienna: I-Tech Education and Publishing.
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005). Overview of the face recognition grand challenge. In Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (pp. 947–954). Washington: IEEE Computer Society. doi:10.1109/CVPR.2005.268.
Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1988). Numerical recipes in C. The art of scientific computing. Cambridge: Press Syndicate of the University of Cambridge. Chap. 10.
Queirolo, C. C., Silva, L., Bellon, O. R., & Segundo, M. P. (2010). 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 206–219. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.14.
Ross, A. A., Jain, A. K., & Nandakumar, K. (2006a). Levels of fusion in biometrics. In International series on biometrics (Vol. 6, pp. 59–90). Berlin: Springer. Chap 4. doi:10.1007/0-387-33123-9.
Ross, A. A., Jain, A. K., & Nandakumar, K. (2006b). Score level fusion. In International series on biometrics (Vol. 6, pp. 91–142). Berlin: Springer. Chap. 5. doi:10.1007/0-387-33123-9.
Salah, A. A., Alyüz, N., & Akarun, L. (2007). Alternative face models for 3D face registration. In Proceedings of the SPIE conference on vision geometry XV, San Jose, CA, USA (Vol. 6499). Berlin: Springer. doi:10.1117/12.705860.
Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., & Akarun, L. (2008). Bosphorus database for 3D face analysis. In B. Schouten, N. C. Juul, A. Drygajlo, & M. Tistarelli (Eds.), Lecture notes in computer science: Vol. 5372. Biometrics and identity management (pp. 47–56). Berlin: Springer. Chap 6. doi:10.1007/978-3-540-89991-4_6.
Scheenstra, A., Ruifrok, A., & Veltkamp, R. C. (2005). A survey of 3D face recognition methods. In Lecture notes in computer science (pp. 891–899). Berlin: Springer.
Silva, L., Bellon, O. R. P., & Boyer, K. L. (2005). Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 762–776. doi:10.1109/TPAMI.2005.108.
Spreeuwers, L. J., Boom, B. J., & Veldhuis, R. N. J. (2007). Better than best: matching score based face registration. In R. N. J. Veldhuis & H. S. Cronie (Eds.), Proceedings of the 28th symposium on information theory in the Benelux, Enschede, The Netherlands (pp. 125–132). Eindhoven: Werkgemeenschap voor Informatie- en Communicatietheorie.
Tang, X., Chen, J., & Moon, Y. (2008). Accurate 3D face registration based on the symmetry plane analysis on nose regions. In Proceedings of the 16th European signal processing conference (EUSIPCO 2008), Lausanne, Switzerland.
Tao, Q., & Veldhuis, R. N. J. (2007). Optimal decision fusion for a face verification system. In L. Seong-Whan & Z. L. Stan (Eds.), Image processing, computer vision, pattern recognition. Proceedings of the 2nd international conference on biometrics. Seoul, Korea (pp. 958–967). Berlin: Springer.
Tao, Q., & Veldhuis, R. N. J. (2008). Hybrid fusion for biometrics: Combining score-level and decision-level fusion. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition, workshop on biometrics (pp. 1–6). Anchorage: IEEE Computer Society Press.
Tao, Q., & Veldhuis, R. N. J. (2009). Threshold-optimized decision-level fusion and its application to biometrics. Pattern Recognition, 42(5), 823–836.
Tao, Q., van Rootseler, R. T. A., Veldhuis, R. N. J., Gehlen, S., & Weber, F. (2007). Optimal decision fusion and its application on 3D face recognition. In A. Bromme, C. Busch, & D. Huhnlein (Eds.), Proceedings of the special interest group on biometrics and electronic signatures, Darmstadt, Germany, Gesellschaft fur Informatik e.V. (pp. 15–24). Berlin: GI-Edition.
Torr, P. H. S., & Zisserman, A. (2000). Mlesac: a new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78, 138–156. doi:10.1006/cviu.1999.0832.
Veldhuis, R. N. J., Bazen, A. M., Booij, W. D. T., & Hendrikse, A. J. (2006). Hand-geometry recognition based on contour landmarks. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nurnberger, & W. Gaul (Eds.), Studies in classification, data analysis, and knowledge organisation. Proceedings of the 29th annual conference of the Gesellschaft fur Klassifikation e.V., Magdeburg, Germany (pp. 646–653). Berlin: Springer.
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Spreeuwers, L. Fast and Accurate 3D Face Recognition. Int J Comput Vis 93, 389–414 (2011). https://doi.org/10.1007/s11263-011-0426-2
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DOI: https://doi.org/10.1007/s11263-011-0426-2