Long-term people reidentification using anthropometric signature
Anthropometric biometrics are the most suitable features for long-term people reidentification. However, feature selection is still a major problem in the anthropometric biometrics literature. In this paper, we aim at improving people reidentification by enhancing feature selection. Based on a statistical analysis of body measurements on a large-scale dataset, a new anthropometric signature is introduced. The proposed signature describes both the size and shape of human body at specific anatomical landmarks. While size is measured by Euclidian distance between four skeleton joint pairs, shape is described by the surface distance along four circular body parts. A novel algorithm is proposed to automatically segment the circular body parts from the subject point cloud using body geometry, cylindrical fitting and soft clustering. The overall system is evaluated on two public datasets using CMC and nAUC metrics. Experimental results show the effectiveness of our method compared to state of the art.