SISTEM ABSENSI BERBASIS PENGENALAN WAJAH MENGGUNAKAN DEEPFACE
Keywords:
Face recognition, DeepFace, Attendance system, Liveness detection, Deep learningAbstract
Manual attendance systems frequently encounter issues such as time fraud, human error, and inefficiencies, particularly in organizations adopting hybrid or remote work models. These limitations reduce the reliability of attendance records and hinder accurate workforce monitoring. To overcome these challenges, a facial recognition-based attendance system was designed and implemented, offering a secure, automated, and contactless alternative to conventional methods.The system leverages state-of-the-art facial recognition technologies, including DeepFace for face verification, RetinaFace for face detection, and FaceNet512 for feature extraction and comparison. In addition to facial biometrics, GPS-based geolocation and liveness detection techniques are integrated to ensure that the individual is not only correctly identified but also physically present during attendance. This multi-factor verification enhances security and minimizes the risk of spoofing or proxy attendance. Performance testing conducted in real-world scenarios indicates a True Acceptance Rate (TAR) of 94% and a False Acceptance Rate (FAR) of 0%, demonstrating high accuracy and robustness. The system presents a practical and efficient solution for modern attendance tracking needs, supporting organizations in improving accountability, compliance, and operational efficiency as part of broader digital transformation initiatives.
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Indonesian Society of Applied Science (ISAS)
