Smart Attendance Automation Using Camera-Based Face Recognition

Attendance management in educational institutions is often performed through manual registers or roll calls, which require staff time and can lead to recording errors, proxy attendance, and difficulty retrieving historical records. This paper presents a web-based Smart Attendance Automation prototype that integrates camera-based face detection and recognition with digital attendance management. The application is developed using Python and Flask, with HTML, CSS, Bootstrap, and JavaScript for the user interface and SQLite for student and attendance records. During enrollment, an administrator enters student details and captures ten facial images per student. The images are organized by roll number and used to train an OpenCV Local Binary Patterns Histograms (LBPH) recognizer. During attendance, browser camera frames are sent to the Flask backend, Haar Cascade detects face regions, and LBPH predicts the registered identity. A configured threshold is applied before a student is accepted. The application checks for an existing record before inserting attendance for the same student and date, and provides student management, daily and monthly reports, individual attendance history, charts, and CSV export. Preliminary testing involved six registered students under good and low lighting, and six unregistered people. The system correctly identified 6/6 registered-student attempts under good lighting and 4/6 under low lighting; the remaining two low-light attempts were rejected. All six unregistered-person tests were rejected as unregistered, with no incorrect acceptance observed. The resulting genuine-student identification rate was 83.33%, the false rejection rate was 16.67%, and the observed false acceptance rate was 0% in this small test sample. These results are preliminary and require validation using a larger, more diverse dataset

Smart attendance, face recognition, OpenCV, Haar Cascade, LBPH, Flask, Python, SQLite, biometric privacy.

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