Identifying individuals from average quality fingerprint reference templates, when the best do not provide the best results !
Résumé
The fingerprint is one of the most used biometric modalities because of its persistence, uniqueness characteristics and ease of acquisition. Nowadays, there are large country-sized fingerprint databases for identification purposes, for border access controls and also for Visa issuance procedures around the world. The objective usually is to identify an input fingerprint among a large fingerprint database. In order to achieve this goal, different fingerprint pre-selection, classification or indexing techniques have been developed to speed up the research process to avoid comparison of the input fingerprint template against each fingerprint in the database. Although these methods are fairly accurate for identification process, we think that all of them assume the hypothesis to have a good quality of the fingerprint template for the first step of enrollment. In this paper, we show how the quality of reference templates can impact the performance of identification algorithms. We collect information and implement differents methods from the state of the art of fingerprint identification. Then, for these differents methods, we vary the quality of reference templates by using NFIQ2 metric quality. This allowed us to build a benchmark in order to evaluate the impact of these different enrollment scenarios on identification.
Origine : Fichiers produits par l'(les) auteur(s)
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