Elapsed Time in Human Gait Recognition: A New Approach

Human gait is an effective biometric source for human identification and visual surveillance; therefore human gait recognition becomes to be a hot topic in recent research. However, the elapsed time problem, which is in its infancy, still receives poor performance. In this paper, we introduce a novel discriminant analysis method to improve the performance. The new model inherits the merits from the tensor rank one analysis, which handles the small samples size problem naturally, and the linear discriminant analysis, which is optimal for classification. Although 2DLDA and DATR also benefit from these two methods, they cannot converge during the training procedure. This means they can be hardly utilized for practical applications. Based on a lot of experiments on elapsed time problem in human gait recognition, the new method is demonstrated to significantly outperform the existing appearance-based methods, such as the principle component analysis, the linear discriminant analysis, and the tensor rank one analysis.