Biometric authentication systems are usually based on features extraction. Features are a collection of measurable details, obtained from the biometric trait that defines the identity of a certain person. This collection of data is known as template, and it's stored in the database.
The acquired biometrics quality must be controlled in order to model the identity of the individual in a unique and distinct way. The creation and update of templates is a critical task for the correct use of a biometric application.
In this paper we propose the implementation of a model that, using biometric-independent tools, intends to update, select and improve the templates stored in the database, in what we have called 'adaptive biometric templates'. It has been tested with a fingerprint biometric database of 60 users. We have obtained an average improvement over traditional templates of 26% for FMR and of 53% for FNMR, we consider these results very successful.