Applying Geometric Function on Sensors 3D Gait Data for Human Identification.

In surveillance system, the video data has received a great deal of attention, instead of Mocap data, there has enough no work on recognizing of human through this data. Most Surveillance system monitors the behavior, activities, or other changing information in surrounding real life; usually it is used to recognize people to the purpose of security issues in society. This paper aims to propose a novel approach of human identification, which based on sensor data acquired by an optical system. 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 suitablei¾?3D static and dynamic joints feature from data. The Parametric Bezier CurvePBC technique applies 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. After that centroid of triangle method apply on constructed triangle to compute a 3D center value. Selecting a triangle which has minimum distance between original pose triangle and recursive triangle center value. We then employed the geometric function to compute the area of each walking pose trianglegait signature. Furthermore, we optimized the gait signature by using statistical moment on computed areas. After an accurate analysis the signature and found that is has a unique relationship among the 3D human gaits, and use this signature as to classify the human identification. The experiments demonstrated on IGS-90 and Vicon motion capture system data that is proved that proposed method is more accurate and reliable results.