Human Identification Using Sensors Data Based on 3D Gait Area

Surveillance means is a system that monitors the behavior, activities, or other changing information, usually it is used to recognize the people for the purpose of security issues in society. This paper aims to propose a novel approach based on sensor data acquired by an optical system for the purpose of human identification. Three joints of the human body, such as the hip, knee, and ankle joint have been selected by the amount of gait movement in this algorithm. By extracting suitable 3D static and dynamic joints feature from data. The method applies the PBC (Parametric Bézier Curve) technique on the extracted features in order to derive the strong correlation between joint movements. The curve control points are used to construct the triangles of each walking pose of human. The geometric function and statistical methods on the triangles are used here to compute the gait signature and analysis of this signature and find the maximum unique relationship among the human gait during the walk, and use it as to classify the human identification. The experimental results demonstrate that this method is more accurate and reliable.