Super-Resolution using Deep Learning to Support Person Identification in Surveillance Video

Recently, video surveillance systems have been perceived as important technical tools that play a fundamental role in protecting people and assets. In particular, the recorded surveillance video sequences are used as evidence to solve violation, theft and criminal cases. Therefore, the identification of the person present on the crime scene becomes a critical task. In this paper, we proposed a Deep learning-based Super-Resolution system that aims to enhance the faces images captured from surveillance video in order to support suspect identification. The proposed system relies on an image processing technique called Super-Resolution that consists of recovering high-resolution images from low-resolution ones. More specifically, we used the Very-Deep Super-Resolution (VDSR) neural network to enhance the image quality. The proposed model was trained with CelebA faces dataset and used to enhance the resolution of the QMUL-SurvFace dataset. It yielded a Peak Signal-to-Noise Ratio (PSNR) improvement of 7% and Structural Similarity Index (SSIM) improvement of 3%. Most importantly, it increased the face recognition rate by 45.7%.