Efficient and privacy-preserving online face recognition over encrypted outsourced data

• Main Authors: YANG, Xiaopeng, ZHU, Hui, LU, Rongxing, LIU, Ximeng, LI, Hui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Face recognition; Online authentication; Outsource; Privacy-preserving; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4412
• Institution: Singapore Management University

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.