3D Face Recognition Based on Curvature Feature Matching, with Expression Variation
In this paper, we try to improve face recognition system by taking better advantage of the inherent 3D nature of the face. Face recognition can be greatly improved because the abundant 3D face features can be obtained from different angles. During the simulation, we try to extract the curvatures of the eyes, nose and mouth, which can be used as features for face recognition. The Gaussian curvature is an important component of our work. The distribution of this curvature is used to construct the feature vectors. In order to raise the recognition rate, the projection method is used to intensify the edge information. The mesh modification method is also applied to the 3D mesh models. Finally, the distance between the 3D normalized curvatures of the features is compared between the query and database images for recognition. Even when the facial expression of the query image has changed, we can still achieve a 92% recognition rate with our 3D face recognition algorithms.
Keywords3d face recognition Gaussian curvature 3d mesh model curvature feature matching
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- 3.Li, L., Liu, F., Li, C., Chen, G.: Realistic wrinkle generation for 3D face modeling based on automatically extracted curves and improved shape control functions. Computers & Graphics (2010) (in press, corrected proof)Google Scholar
- 4.LT 3D Face Camera, http://www.ltech.com.tw/
- 6.Ceron, A., Salazar, A., Prieto, F.: Relevance analysis of 3D curvature-based shape descriptors on interest points of the face. In: 2010 2nd International Conference Image Processing Theory Tools and Applications (IPTA), Paris, Frence, July 7-10 (2010)Google Scholar
- 9.Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: 2001 Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
- 11.Peng, J., Li, Q., Kuo, C.-C.J., Zhou, M.: Estimating Gaussian curvatures from 3D meshes. In: Human Vision and Electronic Imaging VIII, vol. 5007, pp. 270–280 (2003)Google Scholar
- 12.Verevka, O., Buchanan, J.W.: Local K-means Algorithm for Colour Image Quantization. M.Sc. Dissertation, Department of Computing Science. University of Alberta, Canada (1995)Google Scholar
- 13.Chen, J., Medioni, G.: Detection, Localization, and Estimation of Edges. IEEE Trans. PAMI PAMI-11(2), 191–198 (1989)Google Scholar
- 14.Zhong, C., Sun, Z., Tan, T.: Robust 3D face recognition using learned visual codebook. Pattern Recognition, 1–6 (2007)Google Scholar