Abstract
A new approach, called Collective Shape Difference Classifier (CSDC), is proposed to improve the accuracy and computational efficiency of 3D face recognition. The CSDC learns the most discriminative local areas from the Pure Shape Difference Map (PSDM) and trains them as weak classifiers for assembling a collective strong classifier using the real-boosting approach. The PSDM is established between two 3D face models aligned by a posture normalization procedure based on facial features. The model alignment is self-dependent, which avoids registering the probe face against every different gallery face during the recognition, so that a high computational speed is obtained. The experiments, carried out on the FRGC v2 and BU-3DFE databases, yield rank-1 recognition rates better than 98%. Each recognition against a gallery with 1000 faces only needs about 3.05 seconds. These two experimental results together with the high performance recognition on partial faces demonstrate that our algorithm is not only effective but also efficient.
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References
Medioni, G., Waupotitsch, R.: Face recognition and modeling in 3D. In: IEEE Int’l Workshop on AMFG (2003)
Chang, K., Bowyer, K., Flynn, P.: A Survey of Approaches and Challenges in 3D and Multi-Modal 2D+3D Face Recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006)
Chang, K.I., Bowyer, K., Flynn, P.J.: Adaptive Rigid Multi-Region Selection for Handling Expression Variation in 3D Face Recognition. In: IEEE Workshop on FRGC (2005)
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. on PAMI 14(2), 239–256 (1992)
Russ, T.D., Koch, M.W., Little, C.Q.: A 2D range Hausdorff approach for 3D face recognition. In: IEEE Workshop on FRGC (2005)
Lu, X., Jain, A.K.: Deformation modeling for robust 3D face matching. In: IEEE Conf. on CVPR (2006)
Chua, C.S., Han, F., Ho, Y.K.: 3D Human Face Recognition Using Point Signature. In: Int’l Conf. on FG (2000)
Gordon, G.G.: Face recognition from depth maps and surface curvature. In: SPIE Conf. on Geometric Methods in Computer Vision (1991)
Wu, Y.J., Pan, G., Wu, Z.H.: Face Authentication based on Multiple Profiles Extracted from Range Data. In: Int’l Conf. on AVBPA (2003)
Wang, Y.M., Pan, G., Wu, Z.H.: 3D Face Recognition in the Presence of Expression: A Guidance-based Constraint Deformation Approach. In: IEEE Conf. on CVPR (2007)
Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three dimensional face recognition. Int’l Journal of Computer Vision 64(1), 5–30 (2005)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Annual Conf. on Computational Learning Theory (1998)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE Conf. on CVPR (2005)
Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3D facial expression database for facial behavior research. In: Int’l Conf. on FG (2006)
Kakadiaris, I.A., Passalis, G., Toderici, G., et al.: Three-Dimensional Face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Trans. on PAMI 29(4), 640–649 (2007)
Moreno, A.B., Sanchez, A., Velez, J.F., et al.: Face recognition using 3D surface-extracted descriptors. In: Irish Machine Vision and Image Processing Conference (2003)
Mian, A., Bennamoun, M., Owens, R.: An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face recognition. IEEE Trans. on PAMI 29(11), 1927–1943 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. on CVPR (2001)
Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Trans. on PAMI 26(9), 1222–1228 (2004)
Wang, X., Tang, X.: Unified subspace analysis for face recognition. In: IEEE International Conference on Computer Vision (2003)
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Wang, Y., Tang, X., Liu, J., Pan, G., Xiao, R. (2008). 3D Face Recognition by Local Shape Difference Boosting. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_46
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DOI: https://doi.org/10.1007/978-3-540-88682-2_46
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