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

• Published in: Computers & electrical engineering Vol. 85; pp. 106700 - 11
• Main Authors: Deng, Xing, Da, Fepeng, Shao, Haijian, Jiang, Yingtao
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.07.2020; Elsevier BV
• Subjects: 3D face recognition; Algorithms; Classifiers; Covariance; Face recognition; Local covariance descriptor; Locality-sensitive Riemannian kernel; Multi-scale fusion; Occlusion; Representations; Sparse representation; Local covariance descriptor; Sparse representation; 3D face recognition; Multi-scale fusion; Locality-sensitive Riemannian kernel
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2020.106700

•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. [Display omitted] 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%.