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Face recognition using SIFT features under 3D meshes

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Abstract

Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform (SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis (PCA). Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.

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Authors and Affiliations

  1. Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 201800, China

    Cheng Zhang  (张诚), Yu-zhang Gu  (谷宇章) & Ying-guan Wang  (王营冠)

  2. Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000, China

    Ke-li Hu  (胡珂立)

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  1. Cheng Zhang  (张诚)
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  2. Yu-zhang Gu  (谷宇章)
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  3. Ke-li Hu  (胡珂立)
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  4. Ying-guan Wang  (王营冠)
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Correspondence to Cheng Zhang  (张诚).

Additional information

Foundation item: Project(XDA06020300) supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences; Project(12511501700) supported by the Research on the Key Technology of Internet of Things for Urban Community Safety Based on Video Sensor networks

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Zhang, C., Gu, Yz., Hu, Kl. et al. Face recognition using SIFT features under 3D meshes. J. Cent. South Univ. 22, 1817–1825 (2015). https://doi.org/10.1007/s11771-015-2700-x

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