基于相关分析的步态识别

Human gait identification aims to identify people by a sequence of walking images. Comparing with fingerprint or iris based identification the most important advantage of gait identification is that it can be done in a distance. In this paper silhouette correlation analysis based human identification approach is proposed. By background subtracting algorithm the moving silhouette figure can be extracted from the walking images sequence. Every pixel in the silhouette has three dimensions: horizontal axis(x), vertical axis(y) and temporal axis(t). By moving every pixel in the silhouette image along these three dimensions we can get a new silhouette. The correlation result between the original silhouette and the new one can be used as the raw feature of human gait. Discrete Fourier transform is used to extract features from this correlation result. Then these features are normalized to minimize the affection of noise. Primary component analysis method is used to reduce the features' dimensions. Experiment based on CASIA database shows this method has an encouraging recognition performance.