How Image Degradations Affect Deep CNN-Based Face Recognition?

By Samil Karahan, Merve Kilinc Yildirim, Kadir Kirtaç, Ferhat Sükrü Rende, Gultekin Butun, Hazim Kemal Ekenel. 2016

134 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Safety, Robustness & Interpretability

In Signal Processing

Citations in the corpus

On Robustness and Transferability of Convolutional Neural Networks (2020, background): “Early investigations in face and natural image recognition found that performance degrades by introducing blur, Gaussian noise, occlusion, and compression artifacts, but less by color distortions [12, 35].”
Toward Causal Representation Learning (2021, background): “In computer vision [76], [228], changes in the test distribution may, for instance, come from aberrations, such as camera blur, noise, or compression quality [107], [129], [170], [206], or from shifts, rotations, or viewpoints [7], [12], [64], [282].”
Towards Causal Representation Learning (2021, background): “In computer vision [75, 228], changes in the test distribution may, for instance, come from aberrations like camera blur, noise or compression quality [106, 129, 170, 206], or from shifts, rotations, or viewpoints [7, 11, 63, 282].”
Examining the Impact of Blur on Recognition by Convolutional Networks (2016, neutral): “[9] perform a similar evaluation for face-recognition, while Ull-man et al .”
Effects of Image Degradations to CNN-based Image Classification (2018, methodology): “In [12], only the inﬂuence of special degradations to face recognition was analyzed when using deep CNN-based approaches.”
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, background): “[136] found Gaussian noise with its standard deviation”