Evaluation of Benchmark Datasets and Deep Learning Models with Pre-Trained Weights for Vision-Based Dynamic Hand Gesture Recognition

The integration of dynamic hand gesture recognition in computer vision-based systems promises enhanced human–computer interaction, providing a natural and intuitive way of communicating. However, achieving real-time performance efficiency is a highly challenging task. As the effectiveness of dynamic hand gesture recognition is dependent on the nature of the underlying datasets and deep learning models, selecting a diverse and effective dataset and a deep learning model is crucial to achieve reliable performance. This study explores the effectiveness of benchmark hand gesture recognition datasets in training lightweight deep learning models for robust performance. The objective is to evaluate and analyze these datasets and models through training and evaluation for use in practical applications. For the evaluation of these datasets and models, we analyze the models’ performances by evaluation metrics, such as precision, recall, F1-score, specificity, and accuracy. For an unbiased comparison, both subjective and objective metrics are reported, thus offering significant insights on understanding dataset–model interactions in hand gesture recognition.