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.

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In this paper, we describe the world's largest gait database with real-life carried objects (COs), which has been made publicly available for research purposes, and its application to the performance evaluation of vision-based gait recognition. Whereas existing databases for gait recognition include at most 4007 subjects, we constructed an extremely large-scale gait database that includes 62,528 subjects, with an equal distribution of males and females, and ages ranging from 2 to 95 years old. Moreover, whereas existing gait databases consider a few predefined CO positions on a subject's body, we constructed a database that contained unconstrained variations of COs being carried in unconstrained positions. Additionally, gait samples were manually classified into seven carrying status (CS) labels. The extremely large-scale gait database enabled us to evaluate recognition performance under cooperative and uncooperative settings, the impact of the training data size, the recognition difficulty level of the CS labels, and the possibility of the classification of CS labels. Particularly, the latter two performance evaluations have not been investigated in previous gait recognition studies.

Gait-based age estimation has been extensively studied for various applications because of its high practicality. In this paper, we propose a gait-based age estimation method using convolutional neural networks (CNNs). Because gait features vary depending on a subject's attributes, i.e., gender and generation, we propose the following three CNN stages: (1) a CNN for gender estimation, (2) a CNN for age-group estimation, and (3) a CNN for age regression. We conducted experiments using a large population gait database and confirm that the proposed method outperforms state-of-the-art benchmarks.