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Two-level evaluation on sensor interoperability of features in fingerprint image segmentation.

Yang G, Li Y, Yin Y, Li YS - Sensors (Basel) (2012)

Bottom Line: Features used in fingerprint segmentation significantly affect the segmentation performance.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China. gpyang@sdu.edu.cn

ABSTRACT
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.

No MeSH data available.


Features’ histograms of fingerprints in 9 different sub-databases of FVC2000, FVC2002 and FVC2004.
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f3-sensors-12-03186: Features’ histograms of fingerprints in 9 different sub-databases of FVC2000, FVC2002 and FVC2004.

Mentions: Figure 3 provides the features’ histograms of all the 90 fingerprints. In comparison with Figure 2, the overlaps of every feature become more and more complex as the number of different sensors increases. For segmentation features, the more different sensors fingerprints originating from, the more difficult to separate the foreground blocks from the background blocks. This is primarily caused by the sensor interoperability problem.


Two-level evaluation on sensor interoperability of features in fingerprint image segmentation.

Yang G, Li Y, Yin Y, Li YS - Sensors (Basel) (2012)

Features’ histograms of fingerprints in 9 different sub-databases of FVC2000, FVC2002 and FVC2004.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376633&req=5

f3-sensors-12-03186: Features’ histograms of fingerprints in 9 different sub-databases of FVC2000, FVC2002 and FVC2004.
Mentions: Figure 3 provides the features’ histograms of all the 90 fingerprints. In comparison with Figure 2, the overlaps of every feature become more and more complex as the number of different sensors increases. For segmentation features, the more different sensors fingerprints originating from, the more difficult to separate the foreground blocks from the background blocks. This is primarily caused by the sensor interoperability problem.

Bottom Line: Features used in fingerprint segmentation significantly affect the segmentation performance.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China. gpyang@sdu.edu.cn

ABSTRACT
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.

No MeSH data available.