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
Direct Gray-Scale Minutiae Detection In Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality Measures of Fingerprint Images
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
A Neural Network Fingerprint Segmentation Method
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Segmentation of fingerprint images using linear classifier
EURASIP Journal on Applied Signal Processing
Fingerprint image segmentation based on gaussian-hermite moments
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Direct Pore Matching for Fingerprint Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
An efficient watermarking technique for the protection of fingerprint images
EURASIP Journal on Information Security
K-means based fingerprint segmentation with sensor interoperability
EURASIP Journal on Advances in Signal Processing
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A critical step in automatic fingerprint recognition is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract blockwise features. Supervised method usually first extracts point features like coherence, average gray level, variance and Gabor response, then a Fisher linear classifier is chosen for classification. This method provides accurate results, but its computational complexity is higher than most of unsupervised methods. This paper proposes using Harris corner point features to discriminate foreground and background. Shifting a window in any direction around the corner should give a large change in intensity. We observed that the strength of Harris point in the foreground area is much higher than that of Harris point in background area. The underlying mechanism for this segmentation method is that boundary ridge endings are inherently stronger Harris corner points. Some Harris points in noisy blobs might have higher strength, but it can be filtered as outliers using corresponding Gabor response. The experimental results proved the efficiency and accuracy of new method are markedly higher than those of previously described methods.