Adaptive robust sparse representation for face recognition based on weighted and fusion dictionary

Changming Song, Yang Zhou and Wenguang Ji

PLOS ONE, 2026, vol. 21, issue 6, 1-25

Abstract: We propose a new model for face recognition under insufficient sampling conditions in this paper. In the proposed method, we combine the fusion dictionary with nuclear norm regularization to preserve the details of the restored images, and adopt a Laplacian-uniform mixture function to fit the error distribution. Since the proposed model is convex and separable, we employ the classic alternating direction method of multipliers to solve it by introducing auxiliary variables to transform the original problem into the saddle point problem. Theoretically, we conduct the convergence analysis of the proposed numerical algorithm. Final experimental comparisons are provided to verify the satisfactory performance of the proposed model, which outperforms other related competitive methods in both recognition rate and the robustness.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0351984 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 51984&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0351984

DOI: 10.1371/journal.pone.0351984

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().