Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
This work addresses the open issue of quality assessment in biometrics, specifically for fingerprint recognition, by improving verification accuracy through quality-aware fusion, though it appears incremental as it builds on existing methods like NFIQ and introduces novel but specific fusion schemes.
The paper tackled the problem of automatic fingerprint image-quality assessment by using orientation tensors and symmetry descriptors to quantify impairments like noise and blur, and applied these quality measurements to adapt fusion parameters in multialgorithm verification, resulting in boosted recognition rates as shown in experiments on public databases.
Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained 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, is 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).