Side Information for Face Completion: a Robust PCA Approach

TL;DR

This paper enhances robust PCA for face completion by incorporating side information and generative adversarial networks, improving accuracy and efficiency in handling corrupted and missing data in face recognition tasks.

Contribution

It introduces two RPCA models that utilize domain-dependent prior knowledge and side information, along with a GAN-based method for extracting such information, advancing face completion techniques.

Findings

Improved face completion accuracy with side information.

Faster recovery process on large-scale data.

Effective on synthetic and real-world datasets.

Abstract

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.