H3S: A Hospital Staff Sign-in System Based on Face Recognition

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Abstract
There are many problems in traditional hospital staff sign-in systems, such as low efficiency, time required to obtain result statistics, and fraud. A system based on face recognition is a very promising strategy. In 2022, the highest accuracy of face recognition algorithms around the world has exceeded 99%, with fewer than one-thousandth of one percent false positives. Face recognition technology (FRT) has been widely used in finance, intelligent security, access control, and many other fields, and the market has seen sustained rapid growth. Baidu's face recognition algorithm has won a leading place in the most authoritative public evaluation competition in the world. In this study, a hospital staff sign-in system (H3S) has been designed and developed using the Baidu AI face recognition software development kit (SDK) along with PyQt, Python, MySQL, JSON, and other tools. Compared to traditional sign-in systems, the proposed H3S was more convenient, non-contact, highly efficient and accurate, easy for data analysis, and resistant to fraud. After its deployment, sign-in efficiency has been significantly improved, checking and analysis of sign-in data were more convenient and accurate, and fraud has been essentially eliminated.
Funding Information
  • National Science Foundation Youth Science Foundation Project (82102186)