Gait Modeling for Human Identification

Human gait is a kind of dynamic biometrical feature which is complex and difficult to imitate, it is unique and more secure than static features such as password, fingerprint and facial feature. Analyzing people walking patterns, their "step-prints", can lead to the recognition of personal identity. In this paper, we propose to design, build, calibrate, analyze, and use wearable intelligent shoes; then focus on classifying the wearers into authorized ones and unauthorized ones by modeling their individual gait performance. Firstly the intelligent shoes for collecting and modeling human gait to measure an unprecedented number of parameters relevant to gait are presented. Then we introduce cascade neural networks with node-decoupled extended Kalman filtering (CNN-NDEKF) from the paper by Nechyba and Xu (1997) to apply for modeling and classifier generation. Finally, the experimental results of learning algorithms and comparison are described and verify that the proposed method is valid and useful for human identification.