Facial Recognition Software for Employee Presence Using Convolutional Neural Network with InceptionV3 Architecture

Nicholas Nicholas; Muhammad Ezar Al Rivan

doi:10.47709/brilliance.v5i2.6769

Authors

Nicholas Multi Data Palembang University, Indonesia
Muhammad Ezar Al Rivan Multi Data Palembang University, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v5i2.6769

Keywords:

Convolutional Neural Network, Facial Recognition, InceptionV3, Presence, Transfer Learning

Abstract

Presence is a crucial aspect of human resource management that involves recording and monitoring employee attendance. It serves not only for tracking presence but also as the foundation for salary calculation, performance evaluation, and strategic decision-making. While many companies still adopt manual presence systems due to their simplicity, such methods are inefficient, prone to human error, and burdensome in administrative tasks, especially in the presence of growing operational complexity. Moreover, even digital systems like fingerprint scanners are often inflexible, as they require physical presence at designated devices, making them unsuitable for remote or mobile employees. This research developed an Android-based presence application utilizing facial recognition technology with the Convolutional Neural Network method using the InceptionV3 architecture. The system is designed to enable automatic, flexible, and accurate attendance recording both inside and outside the workplace. A website-based system has also been developed for centralized attendance data management. Implementation results show that the Android-based application successfully enables employees to perform attendance both inside and outside the office using facial recognition technology, eliminating the need for manual documentation. Additionally, the web-based system can automatically record and summarize attendance data, simplifying recapitulation processes and reducing administrative workload. The facial recognition model, trained using gradual transfer learning, achieved an accuracy of 97.86% and F1-Score of 97.55%. This application has significant potential to improve the efficiency and flexibility of corporate attendance systems.

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