Abstract
Some biometric cryptographic applications such as fuzzy identity-based identification (FIBI) and fuzzy commitment require ordered and fixed-length bit-string-like IrisCode as input. However, fingerprint minutiae representation (e.g. ISO minutiae format) is unordered and variable in size. Such a characteristic is inapplicable to the aforementioned applications. One of the feasible solutions is to convert minutiae into ordered and fixed-length bit-string, namely point-to-string conversion. The point-to-string conversion has attracted much attention and a number of proposals have been reported in literature over the past decade. Furthermore, the topic of point-to-string conversion continues to gain the interest from the research community lately. In this chapter, the point-to-string conversion methods proposed in early stage are revisited to be served as a background study. Thereafter, a review of recent development on point-to-string conversion is presented. More specifically, two recently proposed methods (i.e. Kernel-Learning and Bag-of-Minutiae) are introduced in detail. Finally, conclusion is given to summarize the challenges, and future prospect in this research topic.
Original language | English |
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Title of host publication | Bio-Inspired Computing Models and Algorithms |
Publisher | World Scientific Publishing Co. |
Pages | 181-211 |
Number of pages | 31 |
ISBN (Electronic) | 9789813143180 |
ISBN (Print) | 9789813143173 |
DOIs | |
Publication status | Published - 2019 Jan 1 |
Bibliographical note
Publisher Copyright:© 2019 by World Scientific Publishing Co. Pte. Ltd.
All Science Journal Classification (ASJC) codes
- Computer Science(all)