Quantifying the impact of image degradation on Deep Learning models in face recognition systems

Abstract

Significant advancements in computer vision, particularly in facial recognition systems, have been witnessed in recent years. However, it is imperative to comprehend how these systems perform under real-world conditions, specifically when confronted with degraded images. This paper presents a comprehensive analysis of the impact of image degradation on facial recognition systems that rely on deep neural networks. The study evaluates three facial detection algorithms and eight facial recognition algorithms, with experiments conducted on four diverse datasets. A total of 14 types of image degradations, encompassing pure and mixed variations, were employed at six different intensity levels. Three distinct types of image pairs were generated to encompass various scenarios. The primary objective of this research is to enhance the understanding and assessment of facial recognition system outcomes, thereby strengthening the overall analysis of these systems. On average, the models had a minimum impact of 17% and a maximum of 43% for the datasets used in the experiment.