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.

We propose a study to predict health abnormalities by analyzing body temperature and the heart rate variability parameters of pulse waves. The research method firstly selects fever by applying a deep learning model to thermal images, and secondly, extracts heart rate variability from pulse waves through a photo-plethysmograph sensor. It analyzes the relevance of body temperature and health status by dividing the presence or absence of fever cases and comparing parameters related to autonomic nerves and stress cases. As a result of the experiment, the control group with normal body temperature had a mean pulse variability of 37.65, SDNN of 64.83, and RMSSD of 42.24. In contrast, in the experimental group, which consisted of individuals with fever, the average pulse variability was 31.91, the SDNN was 42.34, and the RMSSD was 26.80. Our research expects to be applicable to a thermal imaging system that can measure body temperature and bio-signals together and show the predicted results of health status for fever cases.