SMART ATTENDANCE WITH FACE ANTI-SPOOFING TECHNOLOGY USING HAAR CASCADE CLASSIFIER

Abstract

Traditional attendance systems often encounter challenges in efficiently and accurately recording attendance. This research aims to introduce an innovative solution through the development of an intelligent anti-spoofing attendance system based on facial recognition using the Haar Cascade Classifier method. Designed to overcome the inefficiencies in attendance recording, this system ensures the accuracy of educational staff attendance records. Its development method relies on the Haar Cascade Classifier, employing image processing to detect learned object features, particularly focusing on facial recognition. Research findings indicate that the implementation of this system achieves an average accuracy rate of 98.90% in attendance recording. The facial recognition technology ensures reliable attendance recording with confidence levels exceeding 80%, signifying precise facial identification that addresses various challenges and ensures attendance data integrity. Not only does the system identify educational staff with high accuracy, but it also provides prompt responses for efficient attendance logging and verification. Beyond its technical benefits, this study significantly contributes to the development of smarter and more efficient attendance technology. The system plays a crucial role in enhancing the discipline of educational staff at STMIK IKMI Cirebon and streamlining attendance management and evaluation across educational institutions.