FACE DETECTION FOR ENTERPRISE RESOURCE PLATFORM ATTENDANCE SYSTEM: A COMPARATIVE ANALYSIS

The demand for face detection capabilities in attendance systems has led to various implementations using different algorithms and Enterprise Resource Planning (ERP) platforms. This research aimed to conduct a comparative analysis of three face detection algorithms—Dlib, Haar-Cascade, and MTCNN (Multi-task Cascaded Convolutional Networks)—and implement the most effective solution in an Odoo-based attendance system supporting multiple face detection. The study employed evaluation methodology analyzing real-time video streams, utilizing distinct datasets: a control dataset under standard conditions and a challenge dataset featuring variations in lighting, occlusions, and multiple simultaneous faces. Performance evaluation was based on true positive, false positive, and false negative rates for face detection across both datasets. Results demonstrated significant performance variations: under controller conditions, MTCNN achieved 99.69% detection accuracy compared to Dlib’s 92.92% and Haar-Cascade’s 84.08%, while in challenging environments, MTCNN maintained 60.93% accuracy versus Dlib’s 0.66% and Haar-Cascade’s 2.36%. The significant performance drop in challenging conditions can be attributed to poor lightning conditions, facial occlusions, and the complexity of detecting multiple faces simultaneously. The findings facilitated the development of a custom Odoo attendance module implementing MTCNN, demonstrating potential for improving automated attendance efficiency in organizations while establishing benchmarks for futher development of face recognition-based features within Odoo ERP.