Face recognition is a task that involves comparing two images of a face and determining whether they belong to the same person. Face recognition can be applied in a variety of environments, including surveillance systems. However, the performance of the deep learning model used for face recognition can be affected by the quality of the image. Therefore, recent studies on face recognition using deep learning have suggested taking image quality into consideration. Some studies have used feature norms, which is the L2 norm of extracted features from images using a deep learning model, to measure the image quality. However, previous studies have lacked analysis of why the feature norms correspond to image quality. This thesis presents a new hypothesis that a higher sample's feature norms indicate that the samples are similar to other samples learned by the deep learning model. We also demonstrate that this hypothesis can be used to distinguish noise samples. Additionally, we introduce a new regularization technique that uses noise samples to improve face recognition performance in low-resolution environments.
