Title
Fractal Coding-Based Robust And Alignment-Free Fingerprint Image Hashing
Abstract
Biometric image hashing techniques have been widely studied and seen progressive advancements. However, only a handful of available solutions provide two-factor cancelability while simultaneously satisfying the tradeoff among all criteria of template protection mechanisms. In this paper, we propose a novel scheme for generating a secure and robust hash from a fingerprint image using Fourier-Mellin transform and fractal coding. First, due to its invariance property, Fourier-Mellin transform is incorporated into the domain fingerprint minutiae blocks to provide feature alignment, therein generating a fixed-length minutiae representation for comparison. Then, dimensionality reduction and texture compression are exploited using fractal coding to generate a robust and compact hash for improved security and recognition. The experimental results demonstrate a favorable recognition performance on benchmarked state-of-the-art schemes from FVC2002 and FVC2004 fingerprint databases. The analyses prove our method's robustness and resiliency to security and privacy attacks. Our method also satisfies the revocability and unlinkability criteria of cancelable biometrics.
Year
DOI
Venue
2020
10.1109/TIFS.2020.2971142
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Keywords
DocType
Volume
Fingerprint image hashing, Fourier-Mellin transform, fractal coding, robustness, minutiae features
Journal
15
ISSN
Citations 
PageRank 
1556-6013
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sani M. Abdullahi101.35
Hongxia Wang2157.32
Tao Li3111.82