Paper Info
Title
Key Based Artificial Fingerprint Generation for Privacy Protection
Abstract
With the widespread use of biometrics recognition systems, it is of paramount importance to protect the privacy of biometrics. In this paper, we propose to protect the fingerprint privacy by the artificial fingerprint, which is generated based on three pieces of information, i) the original minutiae positions; ii) the artificial fingerprint orientation; and iii) the artificial minutiae polarities. To make it real-look alike and diverse, we propose to generate the artificial fingerprint orientation by a model taking both the global and local fingerprint orientation into account. Its parameters can be easily guided by an user specific key with simple constraints. The artificial minutiae polarities are generated from the same key, where a block based and a function based approach are proposed for the minutiae polarities generation. These information are properly integrated to form a real-look alike artificial fingerprint. It is difficult for the attacker to distinguish such a fingerprint from the real fingerprints. If it is stolen, the complete fingerprint minutiae feature will not be compromised, and we can generate a different artificial fingerprint using another key. Experimental results show that the artificial fingerprint can be recognized accurately.
Year
DOI
Venue
2020
10.1109/TDSC.2018.2812192
IEEE Transactions on Dependable and Secure Computing
Keywords
DocType
Volume
Computational modeling,Feature extraction,Privacy,Fingerprint recognition,Databases,Iris recognition
Journal
17
Issue
ISSN
Citations 
4
1545-5971
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Li Sheng1386.54
Xinpeng Zhang22541174.68
Zhenxing Qian352539.26
Guorui Feng422323.26
Yanli Ren524724.83