Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image Enhancement Using Filtering Techniques
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Design and implementation of Log-Gabor filter in fingerprint image enhancement
Pattern Recognition Letters
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Fingerprint image processing and minutiae extraction for fuzzy vault
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Fingerprint image enhancement based on a half gabor filter
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A method for fingerprint alignment and matching
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
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The purpose of fingerprint image enhancement is to improve the clarity and quality of its local features. The quality of fingerprint image determines the accuracy and reliability of minutia extraction which also determines the accuracy of an automatic fingerprint recognition system. In this paper, we propose an adaptive image pre-processing approach that can significantly improve poor quality images according to their noise level based on contrast stretching, power-law transformation and Gabor filter. The original image smoothing is performed initially by Gaussian filter, and then processed by the proposed adaptive algorithm, and finally the resultant image is filtered by Gabor filter to get the improved binarized image. Experimental results indicate that the proposed approach improves the quality of image and reduces the noise significantly as compared to other fingerprint image preprocessing approaches. Furthermore, the result of Goodness Index (GI) shows that our proposed approach improves the performance by 9% as compared to conventional Gabor filter based approach and is also better than other reported results. Especially, the performance of GI is improved by more than 40% in some poor quality images.