Lightweight knowledge-based authentication model for intelligent closed circuit television in mobile personal computing

Abstract

In the intelligent CCTV surveillance environment, personal identity is confirmed based on face recognition. However, the recognition rate of the current face recognition technology is still faulty. In particular, face recognition may not work correctly due to various causes such as CCTV shot quality, weather, personal pose and facial expression, hairstyle, lighting condition, and so on. In this case, there is a great risk of exposing an object’s privacy information in the video surveillance environment due to erroneous object judgment. This paper proposes a video surveillance-based access control technique that combines a facial recognition system using CCTV machine learning with radio-frequency identification (RFID). The proposed method is implemented when accurate facial recognition is difficult to achieve due to poor video quality or low levels of similarity against feature vectors, in which cases multi-channel authentication is performed with the use of RFID features available on a mobile device in possession of the individual. The dual-channel authentication approach can still help identify the entity and protect his or her privacy with greater security even if the RFID tags for authentication are breached or accurate facial detection becomes challenging due to various factors such as CCTV video quality deterioration.