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Abstract:
With the development of image processing technology and the pervasiveness of mobile devices, face recognition, which can be used to offer convenient and efficient individual authentication service, has attracted considerable interest in recent years. However, people's concern about their face data being leaked during the face recognition process impedes the flourish of face recognition. To address this problem, we present a novel privacy-preserving online face recognition scheme over encrypted outsourced data, named EPFR. With EPFR, a user can achieve secure, accurate and efficient authentication service without disclosing her/his face data. Specifically, an improved homomorphic encryption technology is introduced to provide an efficient online face recognition service based on the Eigenface algorithm. Through extensive analysis, we show that users' face data are kept confidential during the online face recognition process. In addition, we implement the scheme with a real face database, and simulation results demonstrate that the scheme can be used to provide efficient and accurate online face recognition service.
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2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY
Year: 2018
Page: 366-373
Language: English
Cited Count:
WoS CC Cited Count: 9
SCOPUS Cited Count: 8
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 1
Affiliated Colleges: