Design and Implementation of Automated Attendance System Using Contactless Facial Recognition

In today's digital age, facial recognition technology holds significant importance across various industries. While it may not boast the same level of accuracy as iris or fingerprint recognition, its widespread adoption is largely due to its non-intrusive and contactless nature. Moreover, facial recognition systems offer a promising solution for streamlining attendance procedures in educational institutions, offices, and other settings. By automating the attendance process, these systems alleviate the time-consuming and burdensome nature of manual tracking while also mitigating the risk of proxy attendance. Essentially, the objective of such a system is to leverage facial detection and recognition algorithms to identify students as they enter the classroom, thereby seamlessly marking their attendance. Libraries such as OpenCV and face_recognition were utilized. The system integrates ESP32 and ESP32-CAM hardware components to capture facial images in real- time, ensuring a seamless and efficient attendance-taking process. Using the Python OpenCV library, an accurate and reliable facial recognition algorithm has been developed, enabling the system to accurately identify and authenticate students. Attendance data is securely stored and managed in a cloud-based database, facilitating real-time access for teachers and students to monitor attendance records. A user-friendly application is designed to provide teachers with attendance management capabilities and students with the ability to track their attendance history, enhancing transparency and engagement in the educational process. The objectives were to enhance recognition accuracy, securely store student data, and ensure system compatibility. The outcomes include improved accuracy, contactless attendance logging, secure data storage, scalability, efficiency gains, and comprehensive documentation.