Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Spectral minutiae representations of fingerprints enhanced by quality data
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Fusion of static image and dynamic information for signature verification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Quality-based conditional processing in multi-biometrics: application to sensor interoperability
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Quality-based fingerprint segmentation
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Multiple factors based evaluation of fingerprint images quality
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
Wave atoms based compression method for fingerprint images
Pattern Recognition
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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).