Face Recognition in Low Quality Images: A Survey

TL;DR

This survey reviews recent advances in low-resolution face recognition, emphasizing techniques, datasets, and challenges in real-world scenarios like video surveillance with small, low-quality face images.

Contribution

It provides a comprehensive analysis of recent methods, datasets, and experimental settings for low-resolution face recognition over the past five years.

Findings

Techniques like super resolution and deblurring are commonly employed.

Datasets and experimental setups vary widely across studies.

Unconstrained low-resolution face recognition remains challenging.

Abstract

Low-resolution face recognition (LRFR) has received increasing attention over the past few years. Its applications lie widely in the real-world environment when high-resolution or high-quality images are hard to capture. One of the biggest demands for LRFR technologies is video surveillance. As the the number of surveillance cameras in the city increases, the videos that captured will need to be processed automatically. However, those videos or images are usually captured with large standoffs, arbitrary illumination condition, and diverse angles of view. Faces in these images are generally small in size. Several studies addressed this problem employed techniques like super resolution, deblurring, or learning a relationship between different resolution domains. In this paper, we provide a comprehensive review of approaches to low-resolution face recognition in the past five years. First, a general problem definition is given. Later, systematically analysis of the works on this topic is presented by catogory. In addition to describing the methods, we also focus on datasets and experiment settings. We further address the related works on unconstrained low-resolution face recognition and compare them with the result that use synthetic low-resolution data. Finally, we summarized the general limitations and speculate a priorities for the future effort.