A new method for human gait recognition using temporal analysis

Human gait recognition is the process of identifying individuals by their walking manners. The gait as one of newly coming biometrics has recently gained more and more interests from computer vision researchers. In this paper, we propose a new method for model-free recognition of gait based on silhouette in computer vision sequences. The silhouette shape is represented by a novel approach which includes not only the spatial body contour but also the temporal information. First, a background subtraction is used to separate objects from background. Then, we represent the spatial shape of walker and their motion by the temporal matrix, and use Discrete Fourier analysis to analyze the gait feature. The nearest neighbor classifier is used to distinguish the different gaits of human. The performance of our approach is tested using different gait databases. Recognition results show this approach is efficient.