Frontiers of Electrical and Electronic Engineering >
3D face recognition based on principal axes registration and fusing features
Received date: 23 Mar 2011
Accepted date: 29 Apr 2011
Published date: 05 Jun 2011
Copyright
A 3D face recognition approach which uses principal axes registration (PAR) and three face representation features from the re-sampling depth image: Eigenfaces, Fisherfaces and Zernike moments is presented. The approach addresses the issue of 3D face registration instantly achieved by PAR. Because each facial feature has its own advantages, limitations and scope of use, different features will complement each other. Thus the fusing features can learn more expressive characterizations than a single feature. The support vector machine (SVM) is applied for classification. In this method, based on the complementarity between different features, weighted decision-level fusion makes the recognition system have certain fault tolerance. Experimental results show that the proposed approach achieves superior performance with the rank-1 recognition rate of 98.36% for GavabDB database.
Hongxia ZHANG , Yanning ZHANG , Zhe GUO , Zenggang LIN , Chao ZHANG . 3D face recognition based on principal axes registration and fusing features[J]. Frontiers of Electrical and Electronic Engineering, 2011 , 6(2) : 347 -352 . DOI: 10.1007/s11460-011-0155-x
1 |
Llonch R S, Kokiopoulou E, Tosic I, Frossard P. 3D face recognition with sparse spherical representations. Pattern Recognition, 2010, 43(3): 824-834
|
2 |
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: Proceedings of IEEE Computer Society Conference on Computer Vision. 2005, 1: 947-954
|
3 |
Bowyer K W, Chang K, Flynn P. A survey of approaches and challenges in 3D and multi-modal 3D+ 2D face recognition. Computer Vision and Image Understanding, 2006, 101(1): 1-15
|
4 |
Yan P, Bowyer K W. A fast algorithm for ICP-based 3D shape biometrics. In: Proceedings of the fourth IEEE Workshop on Automatic Identification Advanced Technologies. 2005, 213-218
|
5 |
Zhang P, Bui T D, Suen C Y. A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognition, 2007, 40(12): 3415-3429
|
6 |
Hullermeier E, Vanderlooy S. Combining predictions in pair wise classification: an optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition, 2010, 43(1): 128-142
|
7 |
Fu Z L. Effective property and best combination of classifier linear combination. Journal of Computer Research and Development, 2009, 46(7): 1206-1216 (in Chinese)
|
8 |
Zhang G P, Zhang Y N, Guo Z. 3D face registration based on accurate principal axes analysis and ICP. Journal of Computer Engineering and Applications, 2006, 42(29): 62-64 (in Chinese)
|
9 |
Khotanzad A, Hong Y H. Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(5): 489-497
|
10 |
Lam L, Suen C Y. Application of majority voting to pattern recognition: an analysis of its behavior and performance. Systems and Humans, 1997, 27(5): 553-568
|
11 |
Kuncheva L I. Combining pattern classifiers: methods and algorithms. IEEE Transactions on Neural Networks, 2007, 18(3): 964-964
|
12 |
Chang C C, Lin C J. Training v-support vector classifiers: theory and algorithms. Neural Computation, 2001, 13(9): 2119-2147
|
13 |
Moreno A B, Sanchez A. GavabDB: a 3D face database. In: Proceedings of the 2nd COST Workshop on Biometrics on the Internet. 2004, 75-80
|
14 |
Guan P, Zhang L M. 3D face recognition based on facial structural angle and local region map. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2008, 41-44
|
15 |
ter Haar F B, Veltkamp R C. 3D face recognition using facial contour curves. In: Proceedings of IEEE International Conference on Shape Modeling and Applications. 2008, 259-260
|
16 |
Mahoor M H, Abdel-Mottaleb M. 3D face recognition based on 3D ridge lines in range data. In: Proceedings of IEE International Conference on Image Processing. 2007, 1: 137-140
|
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