Paper Info
Title
Efficient and Privacy-Preserving Online Face Recognition Over Encrypted Outsourced Data
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.
Year
DOI
Venue
2018
10.1109/Cybermatics_2018.2018.00089
2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Keywords
Field
DocType
Authentication,Face recognition,Face,Encryption,Servers
Facial recognition system,Homomorphic encryption,Authentication,Eigenface,Computer security,Computer science,Server,Image processing,Encryption,Mobile device
Conference
ISBN
Citations 
PageRank 
978-1-5386-7975-3
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Xiaopeng Yang1255.15
Hui Zhu28317.00
Rongxing Lu35091301.87
Ximeng Liu430452.09
Hui Li556528.48