A Novel Method: 3-D Gait Curve for Human Identification

Human identification has been a prominent area in the field of computer vision and artificial intelligence. In this paper, a novel human identification method is proposed which is based on a Cubic Bezier Curve (CBC) and statistical techniques through 3D joint movement data. Data acquisition from motion capture system that provides accurate motion information of body joints in 3D environment. Such type of data has sole properties which distinguishes between images and videos. The simple kalman filter that can be used for removing noise in data, is guided by smooth and compactness manners. The features of the human body joints one upper joint (shoulder) and three lower joints (hip, knee and ankle) are computed by using the statistical moments. These features are used as the control points of the curve. The curve passes through the control points, which describes the relationship among joints muscles in human walking. Statistical techniques are applied to CBC coordinates for human recognition. Here, the rotation angle data of the joints is extracted from Biovison Hierarchical data, because these four joints provide the discriminating confusion of deduced information of human joints for identification through gait curve. The performance of our method is evaluated on CMU database. It achieves 100 % accuracy rate of identification by using the proposed database.