Smart access control system by using sparse representation features

Abstract

The access control system is used in massive scenarios to protect personal property safety, such as dormitory in campus, company office, and smart apartment. The recent access control system use either RFID card or biometric features, such as fingerprint, face. The RFID card may be lost and misused by other persons. The fingerprint features must be collected by touching a sensor, which is improper during the epidemic of Covid‐19. The face identification in access control system is effected by the mask, especially under the current epidemic situation. The research about partial occlusion face recognition is helpful to improve the existing face authentication for access control system. This paper establishes a smart access control system by using partial occlusion face recognition technology. First, the face is captured by a camera. Second, the collected faces are used to establish a face library for constructing low rank sparse model by using nuclear norm to measure the error matrix. Third, the sparse features of faces are used to train a neural network. The new personal face are verified by the learnt neural network model. The experimental results on a public partial occlusion face dataset demonstrate the effectiveness of the proposed access control system.