Efficient 3D face recognition using local covariance descriptor and Riemannian kernel sparse coding. (August 2017)

• Record Type: Journal Article
• Authors: Deng, Xing; Da, Feipeng; Shao, Haijian
• Is Part Of: Computers & electrical engineering. Volume 62(2017)
• Page Start: 81
• Page End: 91
• Publication Date: 2017-08
• Subjects: 3D face recognition -- Local covariance descriptor -- Riemannian kernel sparse coding -- Symmetric positive define matrix; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.01.028
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Different types of the efficient features based on keypoints neighborhood are extracted. Local covariance descriptor is given to represent 3D face. Properly Riemannian kernel sparse coding is used to identify the faces in probe. Graphical abstract: Abstract: 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.