# Side Information for Face Completion: a Robust PCA Approach

arXiv: https://arxiv.org/abs/1801.07580 · HTML: https://mlsanity.com/paper/1801.07580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [rSVDdpd: A Robust Scalable Video Surveillance Background Modelling Algorithm](https://mlsanity.com/paper/2109.10680/) (2021)
- [Transformed ℓ1 Regularizations for Robust Principal Component Analysis: Toward a Fine-Grained Understanding](https://mlsanity.com/paper/2510.03624/) (2025)

## Abstract (arXiv)

Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and five real-world datasets to verify their effectiveness.