Gait recognition using principal curves and neural networks

This paper presents a new method for human model-free gait recognition using principal curves analysis and neural networks. Principal curves are non-parametric, nonlinear generalizations of principal component analysis, and give a breakthrough to nonlinear principal component analysis. Different from the traditional statistical analysis methods, principal curve analysis seeks lower-dimensional manifolds for every class respectively, and forms the nonlinear summarization of the sample features and directions for each class. Neural network with the virtue of its universal approximation property is an outstanding method to model the nonlinear function of principal curve. Firstly, a background subtraction is used to separate objects from background. Secondly, we extract the contour of silhouettes and represent the spatio-temporal features. Finally, we use principal curves and neural networks to analyze the features to train and test gait sequences. Recognition results demonstrate that our method has encouraging recognition performance.