Human body features recognition using 3D scale invariant feature transform

Abstract

Focusing on applications to the three-dimensional (3D) garment computer aided design (CAD)system, a human features recognition method based on 3D scale invariant feature transformation (SIFT) is proposed in this paper. First of all, pre-processing is performed on the 3D scanned human body, which are the noise reduction and the conversion into point cloud format. Then the 3D scale-invariant feature transformation constrained by directional gradient constraints is used to extract the feature points of the human point cloud model, and the measurement results are recorded. Finally, according to definitions of reference points for garment anthropometry and the actual measurement value corresponding to the human body, the comparison and analysis of diverse recognition algorithms is given. Simulation results show that the proposed method in this paper is valid and effective.