TY - JOUR
T1 - Privacy-Constrained Biometric System for Non-Cooperative Users
AU - Jahromi, Mohammad Naser Sabet
AU - Buch-Cardona, Pau
AU - Avots, Egils
AU - Nasrollahi, Kamal
AU - Guerrero, Sergio Escalera
AU - Moeslund, Thomas B.
AU - Anbarjafari, Gholamreza
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
KW - Biometric recognition
KW - Deep learning
KW - Multimodal-based human identification
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=85075464865&partnerID=8YFLogxK
U2 - 10.3390/e21111033
DO - 10.3390/e21111033
M3 - Journal article
SN - 1099-4300
VL - 21
JO - Entropy
JF - Entropy
IS - 11
M1 - 1033
ER -