People Semantic Description and Re-identification from Point Cloud Geometry

The automatic extraction of biometric descriptors of anonymous people is a challenging scenario in camera networks. This task is typically accomplished making use of visual information. Calibrated RGBD sensors make possible the extraction of point cloud information. We present a novel approach for people semantic description and re-identification using the individual point cloud information. The proposal combines the use of simple geometric features with point cloud features based on surface normals. To test the system validity, we have collected a new and challenging dataset using a RGBD sensor in a top view configuration containing up to 63 identities captured in different sessions in different days within a two weeks period. The results achieved outperform the previous literature based exclusively on geometric features for re-identification, providing additionally very promising results in people description related to gender and hair style.