On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Security with Noisy Data: Private Biometrics, Secure Key Storage and Anti-Counterfeiting
Security with Noisy Data: Private Biometrics, Secure Key Storage and Anti-Counterfeiting
Fingerprint verification using spectral minutiae representations
IEEE Transactions on Information Forensics and Security
Incorporating image quality in multi-algorithm fingerprint verification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Practical biometric authentication with template protection
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint-Based Fuzzy Vault: Implementation and Performance
IEEE Transactions on Information Forensics and Security
Fingerprint-Quality Index Using Gradient Components
IEEE Transactions on Information Forensics and Security
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
IEEE Transactions on Information Forensics and Security
Binary spectral minutiae representation with multi-sample fusion for fingerprint recognition
Proceedings of the 12th ACM workshop on Multimedia and security
Embedding cylinder quality measures into minutia cylinder-code based latent fingerprint matching
Proceedings of the on Multimedia and security
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Many fingerprint recognition systems are based on minutiae matching. However, the recognition accuracy of minutiae-based matching algorithms is highly dependent on the fingerprint minutiae quality. Therefore, in this paper, we introduce a quality integrated spectral minutiae algorithm, in which the minutiae quality information is incorporated to enhance the performance of the spectral minutiae fingerprint recognition system. In our algorithm, two types of quality data are used. The first one is the minutiae reliability, expressing the probability that a given point is indeed a minutia; the second one is the minutiae location accuracy, quantifying the error on the minutiae location. We integrate these two types of quality information into the spectral minutiae representation algorithm and achieve a decrease in the Equal Error Rate of over 20% in the experiment.