A Comparative Study of Machine Learning and Automatic Machine Learning Models for Facial Mask Recognition

Abstract

In this study, we compare the performance of traditional machine learning models and automatic machine learning models in facial mask recognition. We use a dataset of images of individuals wearing masks and not wearing masks to train and test the models. We evaluate the models based on accuracy, precision, recall, and F1 score metrics. Our results show that automatic machine learning models achieve similar or slightly better performance than traditional machine learning models but at the cost of longer training time. This study's results can help practitioners select the appropriate model for facial mask recognition based on the trade-off between accuracy and efficiency. Additionally, this study provides insights into the potential of automatic machine learning models in computer vision tasks, specifically in facial mask recognition. The study concludes that while the traditional machine learning models may be more computationally efficient, the automatic machine learning models can offer comparable or better performance in facial mask recognition.