Minutiae-based enhanced fingerprint verification assessment relaying on image quality factors

In this paper we evaluate authentication performance of the minutiae-based fingerprint automatic recognition system, previously proposed D. Simon-Zorita, et al. (2001) and recently completed, with the new large fingerprint image database, MCYT J. Ortega-Garcia, et al. (2002). The scheme includes: image enhancement, characteristic extraction and pattern recognition. The design of this database permits to analyse the influence of the variability factors appearing in the image acquisition phase. We focus in two factors: the finger position over the acquisition sensor, and the quality of the acquired fingerprint images. The analysis is accomplished in cases of supervised and nonsupervised databases. Score normalization is presented as an effective technique to improve the fingerprint verification system performance, yielding highly competitive EERs.