Dr. Xin Li
We propose, as an alternative to current face recognition paradigms, an algorithm using reweighted l₂ minimization, whose recognition rates are not only comparable to the random projection using l₁ minimization compressive sensing method of Yang et al , but also robust to occlusion. Through numerical experiments, reweighted l₂mirrors the l₁solution  even with occlusion. Moreover, we present a theoretical analysis on the convergence of the proposed l₂approach.
"An Iteratively Reweighted Least Square Implementation for Face Recognition,"
The Pegasus Review: UCF Undergraduate Research Journal: Vol. 6:
1, Article 5.
Available at: https://stars.library.ucf.edu/urj/vol6/iss1/5