TRACQUE: An AI-Based Multimodal Attendance System with Predictive Academic Analytics

Abstract: The proliferation of online educational platforms and the demand for increased institutional efficiency necessitate a departure from traditional, fallible attendance systems. Conventional methods, such as manual roll calls and single-mode biometric verification, are inherently susceptible to proxy attendance, consume valuable instructional time, and lack the capacity for proactive academic analysis. This paper presents TRACQUE, an innovative AI-Based Multimodal Attendance System designed to address these critical inefficiencies. TRACQUE integrates robust verification techniques: Face Recognition using LBPH and OpenCV/CV2, Embedded Fingerprint Authentication using an R307S sensor managed by an ESP32 microcontroller, and Barcode Scanning using the ZXing library. This combination creates a highly secure and proxy-proof attendance logging system. The system leverages Machine Learning models, specifically Linear Regression for continuous performance prediction and a Decision Tree Classifier for identifying students at risk of academic underperformance. The architecture ensures real-time data processing and visualization through an intuitive web dashboard built with Python and Flask. Experimental validation reports high accuracy rates, 97.6% for facial recognition and 98.1% for fingerprint accuracy, coupled with a model inference time of approximately 45 ms per face image. By correlating secure attendance logs with internal academic metrics, TRACQUE transforms attendance tracking into a proactive academic management tool, enabling timely, data-driven interventions to enhance student success.

Keywords: Multimodal biometric authentication, face recognition, LBPH, fingerprint verification, machine learning, predictive analytics, attendance system, decision tree classifier, linear regression, early warning system.