Abstract
This paper presents a 3D face recognition system based on geometrically localized facial features. We propose the feature extraction procedure using the geometrical characteristics of a face. We extract three curvatures, eight invariant facial feature points and their relative features. These features are directly applied to face recognition algorithms which are a depth-based DP (Dynamic Programming) and a feature-based SVM (Support Vector Machine). Experimental results show that face recognition rates based on the depth-based DP and the feature-based SVM are 95% for 20 people and 96% for 100 people, respectively.
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References
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proc. the IEEE 83, 705–740 (1995)
Zhao, W., Chellappa, R., Rosenfeld, A., Phllips, P.J.: Face recognition: A survey. CVL Tech. Report, Center for Automation Research, University of Maryland at College Park (2000)
Medioni, G., Waupotitsch, R.: Face recognition and modeling in 3D. In: Proc. the IEEE Int’l Workshop on Analysis and Modeling of Faces and Gestures, pp. 232–233 (2003)
Song, H., Yang, U., Sohn, K.: 3D face recognition under pose varying environments. In: Chae, K.-J., Yung, M. (eds.) WISA 2003. LNCS, vol. 2908, pp. 333–347. Springer, Heidelberg (2004)
Hallian, P.L.: Two-and Three Dimensional patterns of the Face, pp. 202–203. A K Peters LTD., Wellesley
Sakoe, H., Chiba, S.: A dynamic programming approach to continuous speech recognition. In: Proc. the 7th ICA, August 1971, p. 20 (1971)
Sahbi, H., Boujemaa, N.: Robust Face Recognition Using Dynamic Space Warping. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 121–132. Springer, Heidelberg (2002)
Guo, G., Li, S.Z., Chan, K.: Face recognition by Support Vector Machines. In: Proc. the Fourth IEEE International Conference, March 2000, pp. 196–201 (2000)
Pontil, M., Verri, A.: Support Vector Machines for 3D object recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 637–646 (1998)
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Lee, Y., Song, H., Yang, U., Shin, H., Sohn, K. (2005). Local Feature Based 3D Face Recognition. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_95
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DOI: https://doi.org/10.1007/11527923_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
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