Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition

Abstract

Gait recognition, a form of biometric authentication, facilitates the identification of individuals by analyzing their characteristic walking patterns. This approach exhibits superior performance even from distant, low-resolution imagery from security camera footage. Historically, gait recognition methodologies used the entire sequence of a human body silhouette for spatiotemporal characterization. Recent advancements have introduced part-based feature extraction modules derived from the human body’s transverse plane (i.e., horizontal direction) into cross-view gait recognition (CVGR) applications. However, this study reveals the considerable potential of the parts in the sagittal plane (i.e., vertical direction) to enhance discrimination in CVGR. A novel method is proposed that integrates the parts generated according to transverse and sagittal planes utilizing three-dimensional and two-dimensional convolutional neural networks for robust feature extraction. The proposed method comprises a global, horizontal, and vertical part module for capturing fine-grained local details in the horizontal and vertical part directions, and a horizontal and vertical pyramid mapping module for extracting spatial features into the horizontal and vertical pyramid mapping. The consolidated features from both modules enhance CVGR performance, even amidst challenging covariates such as different carried objects and clothing variations, along with uncontrolled walking patterns in the wild. The effectiveness of this method is demonstrated through its implementation on the CASIA-B, OU-MVLP, and Gait3D benchmark datasets, where it exhibits superior gait recognition performance.

Keywords

• Gait recognition
• deep learning
• global and local Part-based
• horizontal and vertical part-based
• horizontal and vertical pyramid mapping