2017/07/21 by Javier Lamar León, J. Lamar-Leon, Raúl Alonso-Baryolo +9 · 1 citation
Computer Science · Engineering · Mathematics · Medicine · Psychology · #Artificial intelligence #Biometrics #Combinatorics #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Gait #Gait Recognition and Analysis #Gait analysis #Geometry #Human Pose and Action Recognition #Mathematics #Medicine #Novelty #Pattern recognition (psychology) #Physical medicine and rehabilitation #Psychology #Signature (topology) #Silhouette #Topological and Geometric Data Analysis #Topology (electrical circuits) #cs.CV
paper · pdf · doi:10.48550/arxiv.1707.06982
arxiv created 2017/07/21 · openalex publication_date 2017/07/21 · arxiv updated 2017/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Gait recognition is an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. In this paper, we present a persistent homology-based method to extract topological features (the so-called \it topological gait signature) from the the body silhouettes of a gait sequence. % It has been used before in several conference papers of the same authors for human identification, gender classification, carried object detection and monitoring human activities at distance. % The novelty of this paper is the study of the stability of the topological gait signature under small perturbations and the number of gait cycles contained in a gait sequence. In other words, we show that the topological gait signature is robust to the presence of noise in the body silhouettes and to the number of gait cycles contained in a given gait sequence. % We also show that computing our topological gait signature of only the lowest fourth part of the body silhouette, we avoid the upper body movements that are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat.