We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The quantitative evaluation is applied to four open-source tools, producing a baseline for future de-identification methods.

A Quantitative Evaluation Framework of Video De-Identification Methods / S. Bursic, A. D'Amelio, M. Granato, G. Grossi, R. Lanzarotti (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). - In: 2020 25th International Conference on Pattern Recognition (ICPR)[s.l] : IEEE, 2021. - ISBN 978-1-7281-8808-9. - pp. 6089-6095 (( Intervento presentato al 25. convegno International Conference on Pattern Recognition (ICPR) tenutosi a on line nel 2021 [10.1109/ICPR48806.2021.9412186].

A Quantitative Evaluation Framework of Video De-Identification Methods

S. Bursic;A. D'Amelio;M. Granato;G. Grossi;R. Lanzarotti
2021

Abstract

We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The quantitative evaluation is applied to four open-source tools, producing a baseline for future de-identification methods.
Settore INF/01 - Informatica
CAR_RIC19RLANZ_01 - Stairway to elders: bridging space, time and emotions in their social environment for wellbeing - LANZAROTTI, RAFFAELLA - CAR_RIC - Bandi Fondazione Cariplo - 2019
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
A_Quantitative_Evaluation_Framework_of_Video_De-Identification_Methods.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 366.76 kB
Formato Adobe PDF
366.76 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/880380
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact