Segmentation of fingerprint images using the directional image
Pattern Recognition
Segmentation of fingerprint images—a composite method
Pattern Recognition
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
A New Segmentation Algorithm for Low Quality Fingerprint Image
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
A Neural Network Fingerprint Segmentation Method
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Two steps for fingerprint segmentation
Image and Vision Computing
Segmentation of fingerprint images using linear classifier
EURASIP Journal on Applied Signal Processing
A Linear Hybrid Classifier for Fingerprint Segmentation
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
Personalized Fingerprint Segmentation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
IEEE Transactions on Information Forensics and Security
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The need for segmentation of low quality fingerprints in forensics, high security and civilian applications is constantly increasing. Most segmentation algorithms proposed in the literature normally deal with separation of the background from the foreground. However, low quality foreground regions must also be removed to lower errors in feature extraction, matching and decision modules. In this research work, a quality based fingerprint segmentation algorithm is proposed. The proposed algorithm is block-wise, it utilizes the auto-correlation matrix of gradients and its eigenvalue to compute the score quality measure of each block. The score quality measures both local contrast and orientation in each block. The threshold is computed by taking the mean for all the scores assigned to each block. It was evaluated on FVC 2002 and NIST High Resolution 27A databases. Its performance compared to other algorithms was evaluated by independent fingerprint quality measure algorithm. The results from both FVC and NIST databases show that the proposed algorithm results are promising.