Signal quality awareness has been found to increase recognition rates and to support decisions in multi-sensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here we study the orientation tensor of fingerprint images to quantify signal impairments like noise, lack of structure, blur, with the help of symmetry descriptors. Especially favorable in Biometrics, strongly reduced reference, but not less information is sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ) as well as human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multi-algorithm fingerprint recognition environment. In this study, several trained and non-trained score level fusion schemes are investigated. A Bayes-based strategy for incorporating experts ’ past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, are presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training)
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