.png){kind=icon}
.png){kind=icon}
Return
Efficient 3D face recognition using local covariance descriptor and Riemannian kernel sparse coding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2017.01.028.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a novel 3D face recognition method using the local covariance descriptor and Riemannian kernel sparse coding in order to accurately evaluate the intrinsic correlation of the extracted features and further improve the 3D face recognition accuracy. Firstly, the keypoints are detected by the farthest point sampling method, and the corresponding keypoint neighborhood is extracted by the specified radius associated with geodesic distance. Then, different types of the efficient features are selected to construct the local covariance descriptor with inherent property. Finally, the appropriate Riemannian kernel sparse coding is used to identify the faces in probe. Experimental evaluation has been performed on two challenging 3D face datasets, FRGC v2.0 and Bosphorus, which indicates that the proposed approach can significantly improve the identification accuracy comparing with other state-of-the-art methods. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
3D face recognition
Local covariance descriptor
Riemannian kernel sparse coding
Symmetric positive define matrix
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.9
Papers:
6.7K
Citations:
1.3W