Abstract
Depth cameras enable long term re-identification exploiting 3D information that captures biometric cues such as face and characteristic 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 recognition from depth data. Both features are affected by the pose of the subject and the distance from the camera. We propose a model to incorporate 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.
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The Florence 3D Re-Id dataset is released for public use at the following link http://www.micc.unifi.it/.
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Bondi, E., Pala, P., Seidenari, L., Berretti, S., Del Bimbo, A. (2018). Long Term Person Re-identification from Depth Cameras Using Facial and Skeleton Data. In: Wannous, H., Pala, P., Daoudi, M., Flórez-Revuelta, F. (eds) Understanding Human Activities Through 3D Sensors. UHA3DS 2016. Lecture Notes in Computer Science(), vol 10188. Springer, Cham. https://doi.org/10.1007/978-3-319-91863-1_3
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DOI: https://doi.org/10.1007/978-3-319-91863-1_3
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