Pala, PietroSeidenari, LorenzoBerretti, StefanoBimbo, Alberto DelTelea, Alex and Theoharis, Theoharis and Veltkamp, Remco2018-04-142018-04-142018978-3-03868-053-61997-0471https://doi.org/10.2312/3dor.20181058https://diglib.eg.org:443/handle/10.2312/3dor20181058In the typical approach, person re-identification is performed using appearance in 2D still images or videos, thus invalidating any application in which a person may change dress across subsequent acquisitions. For example, this is a relevant scenario for home patient monitoring. Depth cameras enable person re-identification exploiting 3D information that captures biometric cues such as face and characteristic dimensions of the body. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition from depth data. Both features are affected by the pose of the subject and the distance from the camera. In this paper, we propose a model to incorporate a robust skeleton representation with a highly discriminative face feature, weighting samples by their quality. Our method combining face and skeleton data improves rank-1 accuracy compared to individual cues especially on short realistic sequences.Computing methodologiesBiometricsComputer vision representations3D imaging10.2312/3dor.2018105895-101