Abstract
3D image data provide several advantages than 2D data for face recognition and overcome many problems with 2D intensity images based methods. In this paper, we propose a novel approach to 3D-based face recognition. First, a novel representation, called intrinsic features, is presented to encode local 3D shapes. It describes complementary non-relational features to provide an intrinsic representation of faces. This representation is extracted after alignment, and is invariant to translation, rotation and scale. Without reduction, tens of thousands of intrinsic features can be produced for a face, but not all of them are useful and equally important. Therefore, in the second part of the work, we introduce a learning method for learning most effective local features and combining them into a strong classifier using an AdaBoost learning procedure. Experimental results are performed on a large 3D face database obtained with complex illumination, pose and expression variations. The results demonstrate that the proposed approach produces consistently better results than existing methods.
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Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys archive 35(4), 399–458 (2003)
Lee, J.C., Milios, E.: Matching Range Images of Human Faces. In: Proc. ICCV 1990, pp. 722–726 (1990)
Gordon, G.G.: Face Recognition Based on Depth and Curvature Features. In: Proc. CVPR 1992, pp. 108–110 (1992)
Yacoob, Y., Davis, L.S.: Labeling of Human Face Components from Range Data. CVGIP: Image Understanding 60(2), 168–178 (1994)
Chua, C.S., Han, F., Ho, Y.K.: 3D Human Face Recognition Using Point Signiture. In: Proc. FG 2000, pp. 233–239 (2000)
Beumier, C., Acheroy, M.: Automatic 3D Face Authentication. Image and Vision Computing 18(4), 315–321 (2000)
Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based Face Surface Recognition Using Spherical Correlation. In: Proc. FG 1998, pp. 372–377 (1998)
Hesher, C., Srivastava, A., Erlebacher, G.: A Novel Technique for Face Recognition Using Range Imaging. In: Inter. Multiconference in Computer Science (2002)
Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. on PAMI 25(9), 1063–1074 (2003)
Dorai, C., Jain, A.K.: COSMOS-A Representation Scheme for 3-D Free-Form Objects. IEEE Trans. on PAMI 19(10), 1115–1130 (1997)
Lu, X., Colbry, D., Jain, A.K.: Three-dimensional Model Based Face Recognition. In: Proc. ICPR 2004, pp. 362–365 (2004)
Lee, M.W., Ranganath, S.: Pose-invariant Face Recognition Using a 3D Deformable Model. Pattern Recognition 36, 1835–1846 (2003)
Wang, Y., Chua, C., Ho, Y.: Facial Feature Detection and Face Recognition from 2D and 3D Images. Pattern Recognition Letters 23, 1191–1202 (2002)
Chang, K.I., Bowyer, K.W., Flynn, P.J.: An Evaluation of Multi-model 2D+3D Biometrics. IEEE Trans. on PAMI 27(4), 619–624 (2005)
Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Expression-Invariant 3D Face Recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 62–70. Springer, Heidelberg (2003)
Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision Templates for Multiple Classifier Fusion: an Experimental Comparon. Patter Recognition 34, 299–314 (2001)
Xu, C., Wang, Y., Tan, T., Quan, L.: Automatic 3D Face Recognition Combining Global Geometric Features with Local Shape Variation Information. In: Proc. FG 2004, pp. 308–313 (2004)
Xu, C., Wang, Y., Tan, T., Quan, L.: Robust Nose Detection in 3D Facial Data Using Local Characteristics. In: Proc. ICIP 2004, pp. 1995–1998 (2004)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The Feret Evaluation Methodology for Face-Recognition Algorithm. IEEE Trans. on PAMI 22(10), 1090–1104 (2000)
Shen, J., Shen, W., Shen, D.: On Geometric and Orthogonal Moments. Inter. Journal of Pattern Recognition and Artificial Intelligence 14(7), 875–894 (2000)
Besl, P.J., Mckay, N.D.: A Method for Registration of 3-D shapes. IEEE Trans. on PAMI 14(2), 239–256 (1992)
Viola, P., Jones, M.: Robust Real-time Object Detection. In: Proc. 2nd Inter. Workshop on Statistical Computional Theories of Vision (2001)
Moghaddam, B., Pentland, A.: Beyond Euclidean Eigenspaces: Bayesian Matching for Vision recognition. Face Recognition: From Theories to Applications, 921 (1998), ISBN 3-540-64410-5
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Xu, C., Tan, T., Li, S., Wang, Y., Zhong, C. (2006). Learning Effective Intrinsic Features to Boost 3D-Based Face Recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744047_32
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DOI: https://doi.org/10.1007/11744047_32
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