AI-Driven Attendance Tracking with Haar Cascade and ResNet

An automated attendance system tracks attendance electronically, typically using facial recognition technology, streamlining record-keeping processes. This work presents a novel approach to attendance management utilizing advanced technologies such as the ResNet-50 algorithm and Haar Cascade for facial detection. The system leverages ResNet’s deep learning architecture to extract unique facial features, combined with Haar Cascade’s robust facial detection capabilities, resulting in efficient and accurate attendance recording and storing the attendance of all the previous days in an SQLite database. From the experimental results, it has been noted that even under occlusion situations, this model can identify nearly every face with 91.6% accuracy in low lighting and occlusion conditions.