Transfer learning for human gait recognition using VGG19: CASIA-A dataset

Abstract

Identification of individuals based on physical characteristics has recently gained popularity and falls under the category of pattern recognition. Biometric recognition has emerged as an effective strategy for preventing security breaches, as no two people share the same physical characteristics. "Gait recognition" specifically refers to identifying individuals based on their walking patterns. Human gait is a method of locomotion that relies on the coordination of the brain, nerves, and muscles. Traditionally, human gait analysis was performed subjectively through visual observations. However, with advancements in technology and deep learning, human gait analysis can now be conducted empirically and without the need for subject cooperation, enhancing the quality of life. Deep learning methods have demonstrated excellent performance in human gait recognition. In this article, the authors employed the VGG19 transfer learning model for human gait recognition. They used the public benchmark dataset CASIA-A for their experimental work, which contains a total of 19,139 images captured from 20 individuals. The dataset was segmented into two different patterns: 70:30 and 80:20. To optimize the performance of the proposed model, the authors considered three hyperparameters: loss, validation loss (val_loss), and accuracy rate. They reported accuracy rates of 96.9% and 97.8%, with losses of 2.71% and 2.01% for the two patterns, respectively.