Face Image Quality Enhancement Study for Face Recognition

TL;DR

This study investigates face recognition performance on low quality images, develops a new protocol, and demonstrates how image enhancement can improve recognition accuracy in challenging conditions.

Contribution

The paper introduces a new protocol for evaluating face recognition on low quality images and assesses the impact of image enhancement techniques.

Findings

Face recognition performance drops significantly on low quality photos.

Image enhancement improves recognition accuracy on low quality images.

The new protocol provides a standardized way to evaluate face recognition in low quality scenarios.

Abstract

Unconstrained face recognition is an active research area among computer vision and biometric researchers for many years now. Still the problem of face recognition in low quality photos has not been well-studied so far. In this paper, we explore the face recognition performance on low quality photos, and we try to improve the accuracy in dealing with low quality face images. We assemble a large database with low quality photos, and examine the performance of face recognition algorithms for three different quality sets. Using state-of-the-art facial image enhancement approaches, we explore the face recognition performance for the enhanced face images. To perform this without experimental bias, we have developed a new protocol for recognition with low quality face photos and validate the performance experimentally. Our designed protocol for face recognition with low quality face images can be useful to other researchers. Moreover, experiment results show some of the challenging aspects of this problem.