Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

被引：306

作者：

Takemura N. ^{[1
]}

Makihara Y. ^{[1
]}

Muramatsu D. ^{[1
]}

Echigo T. ^{[2
]}

Yagi Y. ^{[1
]}

机构：

[1] Osaka University, Ibaraki

[2] Osaka Electro-Communication University, Neyagawa

来源：

IPSJ Transactions on Computer Vision and Applications | 2018年 / 10卷 / 01期

基金：

日本学术振兴会;

关键词：

Gait database; Gait recognition; Multi-view large population performance evaluation;

D O I：

10.1186/s41074-018-0039-6

中图分类号：

学科分类号：

摘要：

This paper describes the world’s largest gait database with wide view variation, the “OU-ISIR gait database, multi-view large population dataset (OU-MVLP)”, and its application to a statistically reliable performance evaluation of vision-based cross-view gait recognition. Specifically, we construct a gait dataset that includes 10,307 subjects (5114 males and 5193 females) from 14 view angles ranging 0° −90°, 180° −270°. In addition, we evaluate various approaches to gait recognition which are robust against view angles. By using our dataset, we can fully exploit a state-of-the-art method requiring a large number of training samples, e.g., CNN-based cross-view gait recognition method, and we validate effectiveness of such a family of the methods. © 2018, The Author(s).

引用

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