Evaluating CNN Models for Gait Recognition: A Study on the CASIA-B Dataset (GUB Journal of Science and Engineering, 2024)
Gait recognition, a form of biometric identification, has garnered considerable interest for its ability to identify individuals from a distance. Yet, its effectiveness depends significantly on the precision of the underlying categorization models. This study thoroughly evaluates many Convolutional Neural Network (CNN) models to determine their effectiveness in properly identifying and categorizing gait patterns. We train and test several CNN architectures using advanced deep learning techniques on the widely known CASIA-B dataset, which is a benchmark dataset for gait recognition research. The analyses involve models like as VGG16, VGG19, NASNetLarge, NASNetMobile, EfficientNetB0, and Xception, each well-known for their effectiveness in picture categorization tasks. We evaluate the performance of these models by conducting thorough experiments and detailed analysis, focusing on accuracy, validation loss, and other pertinent metrics. The Xception model demonstrated the highest accuracy of 97.17% of the models assessed. This model frequently surpasses similar models, demonstrating its strength and effectiveness in precisely recognizing gait patterns. On the other hand, the accuracy of other models varied from 9.78% to 93.85%, demonstrating the variable levels of efficacy among various designs. The findings have significant implications for the creation and implementation of gait recognition systems. The high precision demonstrated by the Xception model highlights the promise of CNN-based methods in progressing gait identification technology. Our work highlights the significance of choosing suitable model architectures for achieving the best performance in gait detection tasks. In the future, research may focus on using larger datasets and exploring other CNN architectures to improve the accuracy and reliability of gait detection systems. Our goal is to enhance gait recognition technology to improve biometric identification for security, surveillance, and healthcare applications.GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 17-26