Performance Evaluation of Model-based Gait on Multi-view Very Large Population Database with Pose Sequences

Weizhi An, Shiqi Yu, Yasushi Makihara, Xinhui Wu, Chi Xu, Yang Yu, Rijun Liao, Yasushi Yagi
2020 IEEE Transactions on Biometrics Behavior and Identity Science  
Model-based gait recognition is considered to be promising due to the robustness against some variations, such as clothing and baggage carried. Although model-based gait recognition has not been fully explored due to the difficulty of human body model fitting and the lack of a large-scale gait database, recent progress in deep learning-based approaches to human body model fitting and human pose estimation is mitigating the difficulty. In this paper, we, therefore, address the remaining issue by
more » ... remaining issue by presenting a large-scale human pose-based gait database, OUMVLP-Pose, which is based on a publicly available multiview large-scale gait database, OUMVLP. OUMVLP-Pose has many unique advantages compared with other public databases. First, OUMVLP-Pose is the first gait database that provides two datasets of human pose sequences extracted by two standard deep learning-based pose estimation algorithms, OpenPose and AlphaPose. Second, it contains multi-view large-scale data, i.e., over 10,000 subjects and 14 views for each subject. In addition, we also provide benchmarks in which different kinds of gait recognition methods, including model-based methods and appearance-based methods, have been evaluated comprehensively. The model-based gait recognition methods have shown promising performances. We believe this database, OUMVLP-Pose, will greatly promote model-based gait recognition in the next few years.
doi:10.1109/tbiom.2020.3008862 fatcat:bnib4kb7zjhypi26gphodmy4bq