Employee Attendance System with Face Recognition

Nourman Satya Irjanto, Rully Oktavia Hermawan


The attendance recording method that uses a fingerprint recognition system is felt to be a means of spreading the covid 19 virus and it is necessary to create a biometric attendance recording system using facial recognition. This study aims to build an employee attendance system that uses the concept of facial recognition because the existing manual attendance system is time consuming and impractical to maintain. This system consists of four phases - database creation, face detection, face recognition, attendance updates. The database is created using employee photos. Face detection and recognition is carried out using the HOG algorithm for face classification and SVM classification for face recognition. Faces are detected and recognized from the live webcam. Attendance will be recorded automatically into the document. The system after being tested has good accuracy, reaching 86.4% in the morning, and 88.8% in the afternoon, which is quite high accuracy and in future research it is hoped that the system can be built online so that employees can access the attendance system anywhere. and reports can be accessed anywhere online.


Employee attendance; facial recognition; HOG; SVM Clasification; Python;

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DOI: http://dx.doi.org/10.30700/jst.v12i2.1148

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