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    Principal Curvature Measures Estimation and Application to 3D Face Recognition

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    Type
    Article
    Authors
    Tang, Yinhang
    Li, Huibin
    Sun, Xiang cc
    Morvan, Jean-Marie
    Chen, Liming
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    VCC Analytics Research Group
    Date
    2017-04-06
    Online Publication Date
    2017-04-06
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/623896
    
    Metadata
    Show full item record
    Abstract
    This paper presents an effective 3D face keypoint detection, description and matching framework based on three principle curvature measures. These measures give a unified definition of principle curvatures for both smooth and discrete surfaces. They can be reasonably computed based on the normal cycle theory and the geometric measure theory. The strong theoretical basis of these measures provides us a solid discrete estimation method on real 3D face scans represented as triangle meshes. Based on these estimated measures, the proposed method can automatically detect a set of sparse and discriminating 3D facial feature points. The local facial shape around each 3D feature point is comprehensively described by histograms of these principal curvature measures. To guarantee the pose invariance of these descriptors, three principle curvature vectors of these principle curvature measures are employed to assign the canonical directions. Similarity comparison between faces is accomplished by matching all these curvature-based local shape descriptors using the sparse representation-based reconstruction method. The proposed method was evaluated on three public databases, i.e. FRGC v2.0, Bosphorus, and Gavab. Experimental results demonstrated that the three principle curvature measures contain strong complementarity for 3D facial shape description, and their fusion can largely improve the recognition performance. Our approach achieves rank-one recognition rates of 99.6, 95.7, and 97.9% on the neutral subset, expression subset, and the whole FRGC v2.0 databases, respectively. This indicates that our method is robust to moderate facial expression variations. Moreover, it also achieves very competitive performance on the pose subset (over 98.6% except Yaw 90°) and the occlusion subset (98.4%) of the Bosphorus database. Even in the case of extreme pose variations like profiles, it also significantly outperforms the state-of-the-art approaches with a recognition rate of 57.1%. The experiments carried out on the Gavab databases further demonstrate the robustness of our method to varies head pose variations.
    Citation
    Tang Y, Li H, Sun X, Morvan J-M, Chen L (2017) Principal Curvature Measures Estimation and Application to 3D Face Recognition. Journal of Mathematical Imaging and Vision. Available: http://dx.doi.org/10.1007/s10851-017-0728-2.
    Sponsors
    This work was supported in part by the French research agency, l’Agence Nationale de Recherche (ANR), through the Biofence project under the Grant ANR-13-INSE-0004-02. Huibin Li was supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 11401464, the China Postdoctoral Science Foundation (No. 2014M560785), and the International Exchange Funds for the Central Universities No. 2014gjhz07.
    Publisher
    Springer Nature
    Journal
    Journal of Mathematical Imaging and Vision
    DOI
    10.1007/s10851-017-0728-2
    Additional Links
    http://link.springer.com/article/10.1007/s10851-017-0728-2
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10851-017-0728-2
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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