Fingerprint image enhancement using filtering techniques
Real-Time Imaging
Fingerprint enhancement using STFT analysis
Pattern Recognition
Technical Communication: A fast fingerprint image enhancement algorithm using a parabolic mask
Computers and Electrical Engineering
Multi-purpose code generation using fingerprint images
ISP'07 Proceedings of the 6th WSEAS international conference on Information security and privacy
A classifier ensemble for face recognition using gabor wavelet features
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Fusion of multiple candidate orientations in fingerprints
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Biometric identification system's performance enhancement by improving registration progress
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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Fingerprint images vary in quality. In order to ensure that the performance of an automatic fingerprint identification system (AFIS) will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement module in the AFIS system. We introduce a new fingerprint enhancement algorithm which decomposes the input fingerprint image into a set of filtered images. From the filtered images, the orientation field is estimated and a quality mask which distinguishes the recoverable and unrecoverable corrupted regions in the input image is generated. The input fingerprint image is adaptively enhanced in the recoverable regions. The performance of our algorithm has been evaluated on an online fingerprint verification system using the MSU fingerprint database containing over 600 fingerprint images. Experimental results show that our enhancement algorithm improves the performance of the online fingerprint verification system and makes it more robust with respect to the quality of input fingerprint images.