Abstract
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.
Highlights
Gait refers to the walking style of an individual, and can be used as a behavioral biometric [28]
4.2 Evaluation criteria We evaluated the accuracy of gait recognition in two modes: identification and verification
We used the cumulative matching curve (CMC) for identification and the receiver operating characteristic curve with z-normalization (z-ROC), which indicates the trade-off between the false rejection rate (FRR) of genuine samples and false acceptance rate (FAR) of imposter samples with varying thresholds for verification
Summary
Gait refers to the walking style of an individual, and can be used as a behavioral biometric [28]. Gait recognition has to overcome some practical issues because of circumstances defined as covariates, such as view, clothing, shoes, carried object (CO), environmental context, aging, or mental condition [30, 40] These covariates should be fully studied for further progress and the development of a practical and robust gait recognition algorithm. Few large-scale databases are available for gait recognition, for example, the OU-ISIR Gait Database, Large Population Dataset [15] and Large Population Dataset with Bag, β version [22], which consider 4,007 and 2,070 subjects, respectively These datasets for gait recognition seem to be sufficient for a conventional machine learning algorithm (e.g., without DL), they are not sufficiently large to efficiently conduct a study using a DL-based approach. Experiments related to COs have not been investigated in previous gait recognition studies
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