Depth cameras enable long term re-identification exploiting 3D information that captures biometric cues such as face and characteris- tic lengths of the body. In the typical approach, person re-identification is performed using appearance, 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. Unfortunately, face and skeleton quality is not always enough to grant a correct recog- nition from depth data. Both features are affected by the pose of the subject and the distance from the camera. We propose a model to in- corporate a robust skeleton representation with a highly discriminative face feature, weighting samples by their quality. Our method improves rank-1 accuracy especially on short realistic sequences. 1
CITATION STYLE
Bondi, E., Pala, P., Seidenari, L., B, S. B., & Bimbo, A. D. (2018). Understanding Human Activities Through 3D Sensors, 10188(May), 29–41. Retrieved from http://link.springer.com/10.1007/978-3-319-91863-1
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