DGE2I-Net: Exploring Depth Gait Energy and Entropy-Based Image Features for Human Gait Classification Using Deep Neural Networks

Abstract

Image-based gait analysis has become an important research area for extracting comprehensive features for classification of gait abnormality. However, traditional Image-based models mostly focus on the pattern analysis from the image sequences, which affects the efficiency of the classification model. To overcome this problem, Gait Energy Image and Gait Entropy Image are used as silhouette-based feature extraction methods. But these silhouette-based methods frequently face challenges due to variations in lighting, background, and clothing conditions. To mitigate these challenges, depth information is captured to represent of gait features using Depth Gait Energy Image (DGEI) and Depth Gait Entropy Image (DGEnI). Due to 3D nature of the depth images, these are not affected by the environmental obstacles. We create a depth gait dataset of 10 subjects to evaluate these features with two Convolutional Neural Network based models. We achieve 96.30% and 98.62% accuracy for classification of human gait with DGEI and DGEnI-features respectively, which significantly outperforms many traditional methods.