A team of scientists have proposed a biometrics-based system that could identify protesters with covered faces.A new paper to be presented at the IEEE International Conference on Computer Vision Workshops (ICCVW) introduces a deep-learning algorithm that could identify an individual even when part of their face is obscured, reports Motherboard. The report notes that the system was able to correctly identify a person concealed by a scarf 67 percent of the time when they were photographed against a “complex” background, which better resembles real-world conditions.The deep-learning algorithm works by outlining 14 key areas of the face, and then trained a deep-learning model to identify them. It has been developed by a team of researchers from Cambridge University, India's National Institute of Technology, and the Indian Institute of Science.The frame-work is evaluated on two facial disguise (FG) datasets with simple and complex, introduced in the paper. The frame- work is shown to outperform the state-of-the-art methods on key-point detection and face disguise classification. It concludes by stating that the large number of images and disguised in the introduced datasets will improve the training of deep learning networks avoiding the need to perform transfer learning.It uses a algorithm that connects the points into a “star-net structure,” and uses the angles between the points to identify a face. The algorithm can still identify those angles even when part of a person's mug is obscured, by disguises including caps, scarves, and glasses.