Unveiling Tomorrow: Forecasting Attendance Trends and Patterns in Face Recognition-Based Attendance Systems through Deep Learning and Machine Learning Techniques

Abstract

In a competitive business environment, creating an efficient workplace is crucial. Integrating digital technology into employee attendance systems is vital due to its significant impact on workforce efficiency and regulatory compliance. This study aims to formulate a predictive model for analyzing attendance patterns within a facial recognition-based attendance system, leveraging methodologies rooted in machine learning (ML) and deep learning (DL) paradigms. This research integrates regression and classification models derived from ML theory with DL techniques to enhance predictive precision. Utilized models include Random Forest, XGBoost, SVM, KNN, and Neural Network. Assessment of model effectiveness involves the evaluation of four metrics: accuracy, precision, recall, and the F1 score. Data collection relies on a facial recognition-based attendance system, trained, and tested within the Google Colab environment using Python. Findings reveal Random Forest and XGBoost as the most precise predictors of timeliness or tardiness among employees, considering age range and other pertinent factors, achieving an accuracy rate of 99%. Random Forest marginally outperforms XGBoost in both accuracy and F1-score by 0.01. This study is notable for its incorporation of attendance system data with ML and DL methodologies to predict attendance patterns based on age and diverse parameters, consequently enhancing decision-making processes and performance management.