Abstract
Abstract
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.
Funder
Japan Society for the Promotion of Science
Core Research for Evolutional Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Computer Vision and Pattern Recognition
Reference45 articles.
1. Bouchrika I, Goffredo M, Carter J, Nixon M (2011) On using gait in forensic biometrics. J Forensic Sci 56(4):882–889.
2. Lynnerup N, Larsen PK (2014) Gait as evidence. IET Biom 3(2):47–54. 6.
3. Iwama H, Muramatsu D, Makihara Y, Yagi Y (2013) Gait verification system for criminal investigation. IPSJ Trans Comput Vis Appl 5:163–175.
4. Kale A, Roy-Chowdhury A, Chellappa R (2003) Towards a view invariant gait recognition algorithm In: Proc. of IEEE Conf. on Advanced Video and Signal Based Surveillance, 143–150.. IEEE, Miami.
5. Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain In: Proc. of the 9th European Conference on Computer Vision, 151–163.. Springer, Berlin, Heidelberg.
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