Department of Computer Science
A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition
© 2014 IEEE. As the usage of fingerprint systems is rolled out on a large scale, scenarios have cross-device matching to allow information exchange and provide compatibility to the existing systems. A score-level calibration for device interoperability will require normalizing scores obtained from different devices so that they can be matched meaningfully and effectively. Conventional methods either assume a homogeneous distribution or model score distribution based on assumptions that may not be valid. In this paper, we circumvent the problem by leveraging correlations among the scores and propose a novel method for biometric score normalization. Our experiments show the promising results.
Source Publication Title
Proceedings 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
San Diego, United States
Link to Publisher's Edition
Gu, F., Wang, Y., & Cheung, Y. (2014). A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition. Proceedings 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 1165-1170. https://doi.org/10.1109/SMC.2014.6974071