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Learning the Spherical Harmonic Features for 3-D Face Recognition
Peijiang Liu; Yunhong Wang; Di Huang; Zhaoxiang Zhang; Liming Chen
AbstractIn this paper, a competitive method for 3-D face recognition (FR) using spherical harmonic features (SHF) is proposed. With this solution, 3-D face models are characterized by the energies contained in spherical harmonics with different frequencies, thereby enabling the capture of both gross shape and fine surface details of a 3-D facial surface. This is in clear contrast to most 3-D FR techniques which are either holistic or feature based, using local features extracted from distinctive points. First, 3-D face models are represented in a canonical representation, namely, spherical depth map, by which SHF can be calculated. Then, considering the predictive contribution of each SHF feature, especially in the presence of facial expression and occlusion, feature selection methods are used to improve the predictive performance and provide faster and more cost-effective predictors. Experiments have been carried out on three public 3-D face datasets, SHREC2007, FRGC v2.0, and Bosphorus, with increasing difficulties in terms of facial expression, pose, and occlusion, and which demonstrate the effectiveness of the proposed method.
KeywordSpherical Harmonics 3-d Face Recognition Feature Selection Spherical Depth Map
Document Type期刊论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Peijiang Liu,Yunhong Wang,Di Huang,et al. Learning the Spherical Harmonic Features for 3-D Face Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2012,22(3):914-925.
APA Peijiang Liu,Yunhong Wang,Di Huang,Zhaoxiang Zhang,&Liming Chen.(2012).Learning the Spherical Harmonic Features for 3-D Face Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,22(3),914-925.
MLA Peijiang Liu,et al."Learning the Spherical Harmonic Features for 3-D Face Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 22.3(2012):914-925.
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