Intelligent Shoes 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 face. In this paper, we present intelligent shoes for human identification under the framework of capturing and analyzing dynamic human gait. By utilizing this dynamic property we focus on the research idea of classifying the wearers into authorized ones and unauthorized ones by modeling their individual gait performance. Each intelligent shoe can detect fourteen realtime gait parameters through walking. Principal component analysis(PCA) will be applied for feature generation and data reduction, and support vector machine(SVM) will be applied for training and classifier generation. The experimental results verify that the proposed method is valid and useful with a success human identification rate about 98%.