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Impact of Singular Point Detection on Fingerprint Matching Performance

Authors: Sharat Chikkerur; Nalini K. Ratha;

Impact of Singular Point Detection on Fingerprint Matching Performance

Abstract

A majority of the minutiae based fingerprint verification algorithms rely on explicit or implicit alignment of the minutiae points for matching the two prints. With no prior knowledge about point correspondences, this becomes a combinatorial problem. Global features of the fingerprints such as the core and delta points represent intrinsic points of reference that can be used to align the two prints and reduce the computational complexity of the matcher. However, automatic extraction of singular points is usually error prone and is therefore not used by existing matchers. But, a systematic study of the impact on matching performance when core/delta points are available has not been done to date. In this paper, we explore the effects of the availability of reliable core and delta points on speed and accuracy of a matching algorithm. Towards this end, we present significant improvements to core and delta point detection algorithm based on complex filtering principles originally proposed by Nilsson et al., (2005). We also present a modified graph based matching algorithm that can run in O(n) time when the reference points are available. We analyse the resulting improvement in computational complexity and present experimental evaluation over FVC2002 database. We show that there is upto 43% improvement (70.2 ms to 39.8 ms) in average verification time and almost no loss in accuracy when reliable core and delta points are used.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
50
Top 10%
Top 10%
Top 10%
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