Enhancing Access Control : practical implementation of facial recognition technology for entry monitoring and database storage
Siikavirta, Ekaterina (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024100826197
https://urn.fi/URN:NBN:fi:amk-2024100826197
Tiivistelmä
According to the American Lost and Found Statistics of 2023, 60% of individuals reported losing an item in the past year, with keys, cell phones, wallets, and sunglasses being the most common. This raises the question: what if physical keys were no longer necessary?
This thesis explores the implementation of facial recognition technology in a security entry system to enhance security, improve access tracking, and eliminate the need for physical keys in secure areas.
The system developed is a software solution running on the Jetson Nano, integrated with a camera, display, electromagnetic lock, and Arduino Uno. The system operates autonomously, with the use of facial recognition technology granting access to authorized users stored in the database. It also includes backup entry methods like an entry code and allows administrators to manage access permissions and review entry logs through a dedicated interface.
The final system demonstrated reliable facial recognition and anti-spoofing capabilities, though with room for improvement in accuracy. This thesis details the system's design, hardware setup, challenges encountered, and suggestions for improvement. The work serves as a foundation for developing autonomous access control systems applicable in different areas
This thesis explores the implementation of facial recognition technology in a security entry system to enhance security, improve access tracking, and eliminate the need for physical keys in secure areas.
The system developed is a software solution running on the Jetson Nano, integrated with a camera, display, electromagnetic lock, and Arduino Uno. The system operates autonomously, with the use of facial recognition technology granting access to authorized users stored in the database. It also includes backup entry methods like an entry code and allows administrators to manage access permissions and review entry logs through a dedicated interface.
The final system demonstrated reliable facial recognition and anti-spoofing capabilities, though with room for improvement in accuracy. This thesis details the system's design, hardware setup, challenges encountered, and suggestions for improvement. The work serves as a foundation for developing autonomous access control systems applicable in different areas