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3D face recognition based on principal axes registration and fusing features |
Hongxia ZHANG, Yanning ZHANG( ), Zhe GUO, Zenggang LIN, Chao ZHANG |
| Shaanxi Provincial Key Laboratory of Speech and Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China |
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Abstract 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.
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| Keywords
3D face recognition
principal axes registration (PAR)
fusion feature
weighted voting
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Corresponding Author(s):
ZHANG Yanning,Email:ynzhang@nwpu.edu.cn
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Issue Date: 05 June 2011
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