In this paper, we propose a ranking-based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed 'Index-of-Max' (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely, Gaussian random projection-based and uniformly random permutation-based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoys several merits. First, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Second, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Third, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.
|Number of pages||15|
|Journal||IEEE Transactions on Information Forensics and Security|
|Publication status||Published - 2018 Feb|
Bibliographical noteFunding Information:
Manuscript received January 11, 2017; revised June 28, 2017; accepted August 30, 2017. Date of publication September 15, 2017; date of current version November 28, 2017. This work was supported in part by the Institute for Information and Communications Technology Promotion (IITP) through the Korean Government (MSIT) under Grant 2016-0-00097 (Development of Biometrics-Based Key Infrastructure Technology for On-line Identification). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Tanya Ignatenko. (Corresponding author: Andrew Beng Jin Teoh.) Z. Jin and Y.-L. Lai are with the School of Information Technology, Advanced Engineering Platform, Monash University, Subang Jaya 46150, Malaysia (e-mail: email@example.com; firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications