Person Re-identification by analyzing Dynamic Variations in Gait Sequences

TL;DR

This paper introduces a novel gait recognition method that analyzes dynamic motion variations using Active Energy Images and affine moment invariants, improving robustness to appearance changes in video-based person identification.

Contribution

The proposed approach leverages dynamic motion analysis with segmented AEI and affine invariants, avoiding reliance on predicted change databases for gait recognition.

Findings

Effective in handling appearance variations

Utilizes segmented AEI for dynamic motion analysis

Achieves promising recognition accuracy on CASIA-B dataset

Abstract

Gait recognition is a biometric technology that identifies individuals in a video sequence by analysing their style of walking or limb movement. However, this identification is generally sensitive to appearance changes and conventional feature descriptors such as Gait Energy Image (GEI) lose some of the dynamic information in the gait sequence. Active Energy Image (AEI) focuses more on dynamic motion changes than GEI and is more suited to deal with appearance changes. We propose a new approach, which allows recognizing people by analysing the dynamic motion variations and identifying people without using a database of predicted changes. In the proposed method, the active energy image is calculated by averaging the difference frames of the silhouette sequence and divided into multiple segments. Affine moment invariants are computed as gait features for each section. Next, matching weights are calculated based on the similarity between extracted features and those in the database. Finally, the subject is identified by the weighted combination of similarities in all segments. The CASIA-B Gait Database is used as the principal dataset for the experimental analysis.