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Open AccessArticle

Privacy-Constrained Biometric System for Non-Cooperative Users

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Visual Analysis of People Laboratory, Aalborg University, 9100 Aalborg, Denmark
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Computer Vision Centre, Universitat Autònoma de Barcelona, 08193 Bellaterra (Cerdanyola), Barcelona, Spain
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iCV Lab, Institute of Technology, University of Tartu, 50411 Tartu, Estonia
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Department of Mathematics and Informatics, Universitat de Barcelona, 08007 Barcelona, Spain
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Department of Electrical and Electronic Engineering, Hasan Kalyoncu University, 27900 Gaziantep, Turkey
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1033; https://doi.org/10.3390/e21111033
Received: 21 September 2019 / Revised: 19 October 2019 / Accepted: 23 October 2019 / Published: 24 October 2019
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance. View Full-Text
Keywords: biometric recognition; multimodal-based human identification; privacy; deep learning biometric recognition; multimodal-based human identification; privacy; deep learning
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S. Jahromi, M.N.; Buch-Cardona, P.; Avots, E.; Nasrollahi, K.; Escalera, S.; Moeslund, T.B.; Anbarjafari, G. Privacy-Constrained Biometric System for Non-Cooperative Users. Entropy 2019, 21, 1033.

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