Ai-Powered Personalized Learning Path Generator

Abstract

In today's educational environment, maintaining accurate and efficient attendance tracking is crucial for ensuring discipline and monitoring student participation. Traditionally, attendance has been recorded manually through roll calls or signature sheets, which are prone to errors, proxy attendance, and administrative delays. Existing automated methods have attempted to address these issues using RFID cards, biometric fingerprint systems, or simple face recognition; however, these solutions often fall short as RFID cards can be easily misused and standalone biometric systems cannot fully eliminate impersonation risks. In the proposed system, a Smart Attendance System using Embedded Technology is developed, integrating face recognition, fingerprint verification, and SMS alert mechanisms into a single secure platform. The system utilizes an ESP32 as the primary controller interfaced with a high-definition camera for face recognition, a fingerprint sensor module for biometric verification, and a Python script is used to send SMS alerts to parents once attendance is successfully marked.

The working process begins when a student approaches the system; the camera captures and verifies the student's face against the database, after which the student scans their fingerprint for second-level authentication. Upon successful verification of both biometrics, the attendance is logged in the database and an SMS notification is sent to the parent, significantly reducing the possibility of proxy attendance and strengthening security. Compared to existing methods, the dual authentication approach ensures more robust identity confirmation, while real-time SMS alerts keep parents informed and engaged. In conclusion, the proposed embedded smart attendance system offers a secure, reliable, and automated solution that not only streamlines attendance management but also enhances transparency and trust between educational institutions, students, and parents.

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