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Representing 3D face from point cloud to face-aligned spherical depth map
Peijiang Liu; Yunhong Wang; Zhaoxiang Zhang
Source PublicationInternational Journal of Pattern Recognition and Artificial Intelligence
2012-06-18
Volume26Issue:1Pages:1-17
AbstractWe propose a novel representation of 3D face shape which is a key step for feature extraction and face recognition. The input of the proposed methods is unstructured point cloud, which determines the wide applicability of the proposed representation. Our contributions mainly include two parts: Spherical Depth Map (SDM) and face alignment based on SDM. SDM, which can be adopted to many applications, is a special kind of range image utilizing the prior anatomical knowledge of human face. Useful characteristics of SDM facilitate face alignment with higher efficiency and accuracy. Experiments conducted on three popular 3D face databases verify the high efficacy and superiority of the proposed method. The accuracy of face alignment is up to 100% with our strategy. The face verification rates based on the standard protocols are all higher than the baseline performance of FRGC2.0.
KeywordShape Representation Spherical Depth Map Face Alignment Pose Normalization Face Recognition
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13224
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Peijiang Liu,Yunhong Wang,Zhaoxiang Zhang. Representing 3D face from point cloud to face-aligned spherical depth map[J]. International Journal of Pattern Recognition and Artificial Intelligence,2012,26(1):1-17.
APA Peijiang Liu,Yunhong Wang,&Zhaoxiang Zhang.(2012).Representing 3D face from point cloud to face-aligned spherical depth map.International Journal of Pattern Recognition and Artificial Intelligence,26(1),1-17.
MLA Peijiang Liu,et al."Representing 3D face from point cloud to face-aligned spherical depth map".International Journal of Pattern Recognition and Artificial Intelligence 26.1(2012):1-17.
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