Title
Skeleton-based Gait Recognition via Robust Frame-level Matching
Abstract
Gait is a useful biometric feature for human identification in video surveillance applications since it can be obtained without subject cooperation. In recent years, model-based gait recognition using a 3D skeleton has been widely studied through view-invariant modeling and kinematic gait analysis. However, existing methods integrate all frame-level feature vectors using the same criterion, even though skeleton information is highly sensitive to changes in covariate conditions such as clothing, carrying, and occlusion. The scheme inevitably reduces the frame-level discriminative power and eventually degrades performance. Instead, we propose a robust frame-level matching method for gait recognition that minimizes the influence of noisy patterns as well as secures the frame-level discriminative power. To this end, we measure the skeleton quality in terms of body symmetry for each frame. Based on the quality, we construct a quality-adjusted cost matrix between input frames and registered frames to prevent matching with noisy patterns. Our two-stage linear matching is then applied to the cost matrix to compute a frame-level discriminative score including similarity and margin. In the end, the identity of a probe is determined by a weighted majority voting scheme via frame-level scores. It enhances the robustness against inaccurate skeleton estimation results by assigning different weights for each frame based on the score. Our approach outperforms the state-of-the-art methods on three public datasets (UPCVgait, UPCVgaitK2, and SDUgait) and a new gait dataset which we create with consideration of unpredictable behaviors while walking. In addition, we demonstrate that our method is robust to skeleton estimation error, partial occlusion, and data loss. The CILgait dataset and MATLAB code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/site/seokeonchoi/gait-recognition</uri> .
Year
DOI
Venue
2019
10.1109/tifs.2019.2901823
IEEE Transactions on Information Forensics and Security
Keywords
Field
DocType
Skeleton,Gait recognition,Three-dimensional displays,Solid modeling,Pattern matching,Feature extraction,Noise measurement
Computer vision,Feature vector,Kinematics,Pattern recognition,Computer science,Feature extraction,Robustness (computer science),Gait analysis,Artificial intelligence,Biometrics,Pattern matching,Discriminative model
Journal
Volume
Issue
ISSN
14
10
1556-6013
Citations 
PageRank 
References 
2
0.38
0
Authors
4
Name
Order
Citations
PageRank
Seokeon Choi1203.60
Jonghee Kim253.26
Wonjun Kim330126.50
Changick Kim496364.78