Depth cameras enable long term re-identification exploiting 3D information that captures biometric cues such as face and body characteristic lengths. People re-identification is otherwise performed using appearance, thus invalidating any application in which a person may change dress between acquisitions. This is a relevant scenario for home patient monitoring for example. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition. Both features are affected by the subject pose and distance from 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.

Long Term Person Re-Identification from Depth Cameras using Facial and Skeleton Data / Bondi, Enrico; Pala, Pietro; Seidenari, Lorenzo; Berretti, Stefano; Del Bimbo, Alberto. - STAMPA. - 10188 LNCS:(2018), pp. 29-41. (Intervento presentato al convegno 2nd International Workshop on Understanding Human Activities through 3D Sensors (UHA3DS'16) tenutosi a Cancun, Mexico nel 4 December, 2016) [10.1007/978-3-319-91863-1_3].

Long Term Person Re-Identification from Depth Cameras using Facial and Skeleton Data

BONDI, ENRICO;PALA, PIETRO;SEIDENARI, LORENZO;BERRETTI, STEFANO;DEL BIMBO, ALBERTO
2018

Abstract

Depth cameras enable long term re-identification exploiting 3D information that captures biometric cues such as face and body characteristic lengths. People re-identification is otherwise performed using appearance, thus invalidating any application in which a person may change dress between acquisitions. This is a relevant scenario for home patient monitoring for example. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition. Both features are affected by the subject pose and distance from 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.
2018
2nd International Workshop on Understanding Human Activities through 3D Sensors (UHA3DS'16)
2nd International Workshop on Understanding Human Activities through 3D Sensors (UHA3DS'16)
Cancun, Mexico
4 December, 2016
Bondi, Enrico; Pala, Pietro; Seidenari, Lorenzo; Berretti, Stefano; Del Bimbo, Alberto
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1080440
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