Biometric template selection and update: a case study in fingerprints |
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Authors: | Umut Uludag [Author Vitae] Anil Jain [Author Vitae] |
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Affiliation: | a Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA b Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA |
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Abstract: | A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a claimed identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques. |
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Keywords: | Biometrics Template selection Template update Fingerprints Clustering Prototype Template aging |
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