Recently, image recognition technology using deep learning has improved significantly, and security systems and home services that use biometric information such as fingerprints, iris scans, and face recognition are attracting attention. In particular, user authentication methods that utilize face recognition have been studied at length. This study presents a visitor authentication technology that uses CCTV with a Jetson Nano and webcam. In the preprocessing phase for face recognition, face data with 7 features that can be identified as a person are collected using CCTV. The collected dataset goes through the annotation process to classify the data, and facial features are detected using deep learning. If there are four or more detected features, the image data is determined to be a person, and the visitor’s face is matched with stored user data in detail using 81 feature vectors. Additionally, the security of the access control system was enhanced by implementing logging functions such as recording the face of the visitor, the number of visitors, and the time of the visit. This paper implements a visitor authentication system using a Jetson Nano and evaluates performance by analyzing the accuracy and detection speed of the system. The tiny-YOLOv3 in the Jetson Nano was effective in real-time verification for the real-time face authentication system with an average detection speed of 6.5 FPS and 86.3% accuracy. Through this study, we designed a system based on deep learning technology that recognizes and authenticates the face of a user during the visitor access process and controls user access.

Background: Information and communication technology development provides services to various fields. In particular, the development of mobile technology has made ubiquitous services possible. However, as technology advances, security is being emphasized more and more. The existing method of user authentication by entering an ID and password is likely to leak sensitive information if the server is attacked or keylogged. Therefore, multi-level authentication is needed to prevent server attacks or keylogging. Methods: Biometric authentication technology has been utilized by smartphones, but it is challenging to apply in the case of a lost device because the central server does not manage biometric data. Encrypting transactions through blockchain technology makes data management more secure because blockchain technology is distributed, and there is no primary target for hackers. Results: The method proposed in this study can become a basis for an authentication method that guarantees both security and integrity due to the synergetic use of biometric and blockchain technologies. Conclusions: The information stored in the service provider does not include sensitive information such as the user’s biometric data but is only a means of verifying the user’s information in the blockchain transaction with guaranteed anonymity. Therefore, users can receive services with confidence and safety.