A multi-scale three-dimensional face recognition approach with sparse representation-based classifier and fusion of local covariance descriptors. (July 2020)

• Record Type: Journal Article
• Authors: Deng, Xing; Da, Fepeng; Shao, Haijian; Jiang, Yingtao
• Is Part Of: Computers & electrical engineering. Volume 85(2020)
• Publication Date: 2020-07
• Subjects: 3D face recognition -- Multi-scale fusion -- Local covariance descriptor -- Locality-sensitive Riemannian kernel -- Sparse representation; 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.2020.106700
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The multi-scale features space representation is developed based on the new 3D faces generated by the Gaussian filter. An analytical solution is calculated and proposed to LASSO problem of locality-sensitive Riemannian kernel sparse representation based classifier. The locality-sensitive Riemannian sparse representation-based classifier is constructed to accurately recognize faces with various expressions, poses and occlusions. Graphical abstract: Abstract: In this paper, an efficient multi-scale hybrid approach is proposed to tackle two main problems in three-dimensional (3D) face recognition, namely the singularity of scale features representation and underexplored locality in dictionary learning. The multi-scale features space representation is developed based on the new 3D faces generated by the Gaussian filter. The locality-sensitive Riemannian sparse representation-based classifier is also constructed to accurately recognize faces with various expressions, poses and occlusions. Two sets of face recognition experiment, one that includes expression variations, and the another that includes pose and occlusion variations, are conducted to compare the performance of the proposed approach against other benchmark 3D face recognition algorithms. The recognition accuracies of the proposed algorithm to both Neutral vs. Neutral achieved on Face Recognition Grand Challenge (FRGC) v2.0 database and Bosphorus database are 100%.