Fingerprint Minutiae to Fixed-Length Bit-String: A Review of Recent Development

Zhe Jin, Wei Jing Wong, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingChapter


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 languageEnglish
Title of host publicationBio-Inspired Computing Models and Algorithms
PublisherWorld Scientific Publishing Co.
Number of pages31
ISBN (Electronic)9789813143180
ISBN (Print)9789813143173
Publication statusPublished - 2019 Jan 1

Bibliographical note

Publisher Copyright:
© 2019 by World Scientific Publishing Co. Pte. Ltd.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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