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Long Term Person Re-identification from Depth Cameras Using Facial and Skeleton Data

  • Enrico Bondi
  • Pietro Pala
  • Lorenzo Seidenari
  • Stefano Berretti
  • Alberto Del Bimbo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10188)

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Enrico Bondi
    • 1
  • Pietro Pala
    • 1
  • Lorenzo Seidenari
    • 1
  • Stefano Berretti
    • 1
  • Alberto Del Bimbo
    • 1
  1. 1.Media Integration and Communication Center - MICCUniversity of FlorenceFlorenceItaly

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