Abstract
Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.
Highlights
Gait has many significant advantages over other forms of biometric
Experiments on the CASIA Gait Dataset B and the OU-MVLP Dataset proves that under limited gait frames being used, the proposed method can achieve a reliable performance for gait recognition
The comparison results indicate that compared with the other two strategies, it is more effective to assemble human silhouettes and skeleton models as input when addressing the problem of insufficient input data for gait recognition, especially in the CL cases
Summary
Gait has many significant advantages over other forms of biometric. Gait can be acquired at a distance in a non-invasive manner without subject cooperation. It is easy to be acquired but hard to be distinguished as walking is a common but unique activity of each subject. Gait still works well even if it is obscured and/or its resolution is low. In Denmark and UK, gait analysis has been used to collect evidences of convicting criminals (Larsen, Simonsen & Lynnerup, 2008; Bouchrika et al, 2011). Many traditional gait recognition approaches have been published. Based on the gait features generated, these approaches can be classified into two categories, including appearance-based methods and model-based methods
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