Segmentation of Fingerprint Image Using Block-Wise Coherence Algorithm

The Segmentation of fingerprint image is an important step in the fingerprint identification. The objective of the fingerprint image segmentation is to separating the foreground regions from the background regions. Accurate segmentation of fingerprint images influences directly the performance of minutiae extraction like minutiae and singular points. In this paper, an algorithm for the segmentation of fingerprint image is presented. The method uses block-wise coherence. Fingerprint data has been taken from NIST databases 14. The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result

Segmentation Of Fingerprint Image Using Block-Wise Coherence Algorithm

The Segmentation of fingerprint image is an important step in the fingerprint identification. The objective of the fingerprint image segmentation is to separating the foreground regions from the background regions. Accurate segmentation of fingerprint images influences directly the performance of minutiae extraction like minutiae and singular points. In this paper, an algorithm for the segmentation of fingerprint image is presented. The method uses block-wise coherence. Fingerprint data has been taken from NIST databases 14. The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result. Keyword: fingerprint image segmentation, block-wise, coherence, minutiae, singular point

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors