Understanding the Impact of Image Quality in Face Processing Algorithms

Abstract

Face processing algorithms are becoming more popular in recent days due to the great domain of application they can be used in. As a consequence, research about the quality of face images is also increasing. Several papers concluded that image quality does impact the performance of face processing algorithms, with low-quality images having a detrimental effect on performance. However, there is still a need for a comprehensive understanding of the extent of the impact of specific distortions like noise, blur, JPEG compression, and brightness. We’ve conducted a study evaluating the performance of three face processing algorithms with images under different levels of the aforementioned distortions. The study’s results placed noise and blur with Gaussian distributions, as the main distortions affecting performance. A detailed description of the adopted methodology, as well as the results obtained from the study, is presented in this paper.