The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition

By Haruyuki Iwama, Mayu Okumura, Yasushi Makihara, Yasushi Yagi. IEEE Transactions on Information Forensics and Security, 2012

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How researchers see this paper

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A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs (2017, methodology): “However, a recent empirical study by Iwama et al. [28] shows that GEI, despite of its simplicity, is the most stable and effective kind of features for gait recognition on their proposed dataset with 4,007 subjects.”
Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition (2018, methodology): “OU-ISIR LP [22] 4016 4 55–85 Indoor 2012”
GEINet: View-invariant gait recognition using a convolutional neural network (2016, methodology): “The only exception is the OU-ISIR large population dataset [13], which contains over 4000 subjects and has view variations from an oblique view to a side view.”
Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis (2016, background): “In recent years, larger human gait databases, e.g., OU-ISIR LP with more than 4000 people [6], have been published to evaluate the upper bound accuracy of gait recognition.”
Pattern Recognition (2018, neutral): “For eﬃcient gait recognition, we consider the same three datasets as [5] , namely CASIA-B [36] , OU-ISIR [37] and USF [38] .”