Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments

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Effects of Degradations on Deep Neural Network Architectures (2018, methodology): “However, in [10], authors did not mention any model that can achieve robust recognition results.”
Understanding how image quality affects deep neural networks (2016, methodology): “Karam and Zhu [9] present a face recognition dataset that considers ﬁve different types of quality distortions.”
Human and DNN Classification Performance on Images With Quality Distortions (2019, background): “Karam and Zhu (2015) present a face recognition dataset for evaluation under five different quality distortions.”