Pose-based Deep Gait Recognition

TL;DR

This paper introduces a novel pose-based deep learning approach for gait recognition that focuses on joint motion and optical flow, outperforming existing methods and demonstrating good generalization across datasets.

Contribution

A new pose-based convolutional neural network model that emphasizes joint motion for gait recognition, with comprehensive architecture comparisons and cross-dataset evaluation.

Findings

Our method outperforms state-of-the-art gait recognition techniques.

Using joint motion around human joints improves recognition accuracy.

The approach generalizes well across different datasets.

Abstract

Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this paper, we present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhouette of a moving person, we consider the motion of points in the areas around human joints. To extract motion information, we estimate the optical flow between consecutive frames. We propose a deep convolutional model that computes pose-based gait descriptors. We compare different network architectures and aggregation methods and experimentally assess various sets of body parts to determine which are the most important for gait recognition. In addition, we investigate the generalization ability of the developed algorithms by transferring them between datasets. The results of these experiments show that our approach outperforms state-of-the-art methods.