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

Noriko Takemura, Yasushi Makihara, Daigo Muramatsu, Tomio Echigo, Yasushi Yagi
2018 IPSJ Transactions on Computer Vision and Applications  
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
more » ... ition 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.
doi:10.1186/s41074-018-0039-6 fatcat:igsiyoirkzeyplingfwdzqrnzi