How Image Degradations Affect Deep CNN-based Face Recognition?

TL;DR

This paper investigates how various image degradations like blur, noise, and occlusion impact the performance of deep CNN-based face recognition systems, revealing their robustness to some distortions but vulnerability to others.

Contribution

It provides a comprehensive analysis of the effects of different image degradations on popular deep CNN face recognition models using standard evaluation protocols.

Findings

Blur, noise, and occlusion significantly reduce recognition accuracy.

Deep CNN models are robust to color distortions and color balance changes.

Performance degradation varies depending on the type of image distortion.

Abstract

Face recognition approaches that are based on deep convolutional neural networks (CNN) have been dominating the field. The performance improvements they have provided in the so called in-the-wild datasets are significant, however, their performance under image quality degradations have not been assessed, yet. This is particularly important, since in real-world face recognition applications, images may contain various kinds of degradations due to motion blur, noise, compression artifacts, color distortions, and occlusion. In this work, we have addressed this problem and analyzed the influence of these image degradations on the performance of deep CNN-based face recognition approaches using the standard LFW closed-set identification protocol. We have evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and GoogLeNet. Results have indicated that blur, noise, and occlusion cause a significant decrease in performance, while deep CNN models are found to be robust to distortions, such as color distortions and change in color balance.