A Real-Time Matching System for Large Fingerprint Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM
Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Fingerprint verification based on minutiae features: a review
Pattern Analysis & Applications
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Fingerprint alignment using a two stage optimization
Pattern Recognition Letters
Score normalization in multimodal biometric systems
Pattern Recognition
A novel verification criterion for distortion-free fingerprints
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Nonparametric fingerprint deformation modelling
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Expert Systems with Applications: An International Journal
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Information fusion is a powerful approach to increasing the accuracy of biometric authentication systems, and is currently an active area of research. The majority of studies focus on combining the results from multiple verification systems at the match score level using either a classification or combination scheme. However, there are advantages to performing the fusion at an earlier stage of processing. Fingerprint registration involves finding the translation and rotation parameters that align two fingerprints; a challenging problem that can be approached in a number of ways. The fusion of fingerprint alignment algorithms is introduced in the form of dynamic registration selection. A Bayesian statistical framework is used to select the most probable alignment produced by competing algorithms. The results of the proposed technique are tested on multiple FVC 2002 databases, and are shown to outperform methods based on match score combination.