Access control by RFID and face recognition based on neural network

Radio frequency identification (RFID) technology has been widely adopted in access control system. However, the people holding the RFID card passing through the access control may not be the authorized one. Therefore, an access control system combining RFID technology and face recognition based on neural network is presented in this work. The system recognizes the face of the person holding the RFID card and denies access if they do not match. We adopt a Radial Basis Function Neural Network (RBFNN) to learn the face of authorized card holders and save the parameters of RBFNN only. This could reduce storage when the number of card holders getting large. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) features are extracted to reduce the dimensions of face image data. The Localized Generalization Error Model (L-GEM) is adopted to train a RBFNN for better generalization capability. The face recognition system is first evaluated by benchmarking ORL face image database. The whole access control system is then tested in a real environment. Experimental results show that the proposed method has a good performance and could improve the security of RFID access control.