Regenerating vital facial keypoints for impostor identification from disguised images using CNN

Technical advancements in the digital era have eased up the time required to complete complex tasks by a decisive rate. These advancements introduce several threats. Hence, there is a compelling need to provide security for systems against these threats. Identity and data thefts are the major threats which require immediate, effective and responsive solutions. Face recognition systems have evolved over the last few years, providing a suitable solution for authenticating an individual. However, the system running on facial recognition can be interfered by disguise. This paper discusses the approaches used in detecting a disguise and analysing the captured face among the other non-disguised faces in the dataset. The parameters used for detection and analysis are the keypoints of the face. These keypoints may be blocked, hidden or disoriented due to the presence of props on the face. The proposed approach aims to avoid this interference by creating an estimate of the face based on fewer available keypoints. The estimated face so created, is then matched with the available set of faces to determine the probabilistic chance of the matched person's presence even though a disguise had been detected. Thus the solution provides a clear distinction if the image obtained proves to be of an impostor or an intruder with obfuscation with a said probability. The Disguised Faces in the Wild (DFW) dataset has been used.