Biometric Recognition Using Deep Learning: A Survey <s> Gait Datasets <s> Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Gait Datasets <s> Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Gait Datasets <s> This paper describes a large-scale gait database comprising the Treadmill Dataset. The dataset focuses on variations in walking conditions and includes 200 subjects with 25 views, 34 subjects with 9 speed variations from 2km/h to 10km/h with a 1km/h interval, and 68 subjects with at most 32 clothes variations. The range of variations in these three factors is significantly larger than that of previous gait databases, and therefore, the Treadmill Dataset can be used in research on invariant gait recognition. Moreover, the dataset contains more diverse gender and ages than the existing databases and hence it enables us to evaluate gait-based gender and age group classification in more statistically reliable way. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Gait Datasets <s> This paper describes the world's largest gait database-the “OU-ISIR Gait Database, Large Population Dataset”-and its application to a statistically reliable performance evaluation of vision-based gait recognition. Whereas existing gait databases include at most 185 subjects, we construct a larger gait database that includes 4007 subjects (2135 males and 1872 females) with ages ranging from 1 to 94 years. The dataset allows us to determine statistically significant performance differences between currently proposed gait features. In addition, the dependences of gait-recognition performance on gender and age group are investigated and the results provide several novel insights, such as the gradual change in recognition performance with human growth. <s> BIB004
Some of the widely used gait recognition datasets include: CASIA Gait Database: This CASIA Gait Recognition Dataset contains 4 subsets: Dataset A (standard dataset) BIB001 , Dataset B (multi-view gait dataset), Dataset C (infrared gait dataset), and Dataset D (gait and its corresponding footprint dataset)  . Here we give details of CASIA B dataset, which is very popular. Dataset B is a large multi-view gait database, which is created in 2005. There are 124 subjects, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered. Besides the video files, they also provide human silhouettes extracted from video files. The reader is referred to BIB002 for more detailed information about Dataset B. Osaka Treadmill Dataset: This dataset has been collected in March 2007 at the Institute of Scientific and Industrial Research (ISIR), Osaka University (OU)  . The dataset consists of 4,007 persons walking on a treadmill surrounded by the 25 cameras at 60 fps, 640 by 480 pixels. The datasets are basically distributed in a form of silhouette sequences registered and sizenormalized to 88x128 pixels size. They have four subsets of this dataset, dataset A: Speed variation, dataset B: Clothes variation, dataset C: view variations, and dataset D: Gait fluctuation. The dataset B is composed of gait silhouette sequences of 68 subjects from the side view with clothes variations of up to 32 combinations. Detailed descriptions about all these datasets can be found in this technical note BIB003 . Osaka University Large Population (OULP) Dataset: This dataset BIB004 includes images from 4,016 subjects from different ages (up to 94 years old) taken from two surrounding cameras and 4 observation angles. The images are normalized to 88x128 pixels.