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Standard Fingerprint Databases: Manual Minutiae Labeling and Matcher Performance Analyses

Fingerprint verification and identification algorithms based on minutiae features are used in many biometric systems today (e.g., governmental e-ID programs, border control, AFIS, personal authentication for portable devices). Researchers in industry/academia are now able to utilize many publicly available fingerprint databases (e.g., Fingerprint Verification Competition (FVC) & NIST databases) to compare/evaluate their feature extraction and/or matching algorithm performances against those of others. The results from these evaluations are typically utilized by decision makers responsible for implementing the cited biometric systems, in selecting/tuning specific sensors, feature extractors and matchers. In this study, for a subset of the cited public fingerprint databases, we report fingerprint minutiae matching results, which are based on (i) minutiae extracted automatically from fingerprint images, and (ii) minutiae extracted manually by human subjects. By doing so, we are able to (i) quantitatively judge the performance differences between these two cases, (ii) elaborate on performance upper bounds of minutiae matching, utilizing what can be termed as "ground truth" minutiae features, (iii) analyze minutiae matching performance, without coupling it with the minutiae extraction performance beforehand. Further, as we will freely distribute the minutiae templates, originating from this manual labeling study, in a standard minutiae template exchange format (ISO 19794-2), we believe that other researchers in the biometrics community will be able to utilize the associated results & templates to create their own evaluations pertaining to their fingerprint minutiae extractors/matchers.

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