TY - JOUR
T1 - A Secure Face Recognition for IoT-enabled Healthcare System
AU - Sardar, Alamgir
AU - Umer, Saiyed
AU - Rout, Ranjeet Kr
AU - Wang, Shui Hua
AU - Tanveer, M.
N1 - Publisher Copyright:
© 2023 Association for Computing Machinery.
PY - 2023/4/17
Y1 - 2023/4/17
N2 - In Healthcare, the Internet of Things (IoT)-enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this article, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three-step template protection techniques are proposed: (i) Cancelable biometrics, (ii) BioCrypto-Circuit, and (iii) BioCrypto-Protection. The performance of the proposed system has been tested on four benchmark face databases, CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.
AB - In Healthcare, the Internet of Things (IoT)-enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this article, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three-step template protection techniques are proposed: (i) Cancelable biometrics, (ii) BioCrypto-Circuit, and (iii) BioCrypto-Protection. The performance of the proposed system has been tested on four benchmark face databases, CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.
KW - BioCryptosystem
KW - Internet of Things
KW - cancelable biometrics
KW - face recognition
KW - healthcare
UR - http://www.scopus.com/inward/record.url?scp=85166329737&partnerID=8YFLogxK
U2 - 10.1145/3534122
DO - 10.1145/3534122
M3 - Article
AN - SCOPUS:85166329737
SN - 1550-4859
VL - 19
JO - ACM Transactions on Sensor Networks
JF - ACM Transactions on Sensor Networks
IS - 3
M1 - 52
ER -