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Robust 3D Face Recognition Using Learned Visual Codebook

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Robust 3D Face Recognition Using Learned Visual Codebook
In this paper, we propose a novel learned visual codebook (LVC) for 3D face recognition. In our method, we first extract intrinsic discriminative information embedded in 3D faces using Gabor filters, then K-means clustering is adopted to learn the centers from the filter response vectors. We construct LVC by these learned centers. Finally we represent 3D faces based on LVC and achieve recognition using a nearest neighbor (NN) classifier. The novelty of this paper comes from 1) We first apply textons based methods into 3D face recognition; 2) We encompass the efficiency of Gabor features for face recognition and the robustness of texton strategy for texture classification simultaneously. Our experiments are based on two challenging databases, CASIA 3D face database and FRGC2.0 3D face database. Experimental results show LVC performs better than many commonly used methods.
Cheng Zhong, Zhenan Sun, Tieniu Tan
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CVPR
Authors Cheng Zhong, Zhenan Sun, Tieniu Tan
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