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

Gongping Yang,Ying Li,Ya-Shuo Li,Yilong Yin

doi:10.3390/s120303186

Gongping Yang, Ying Li + Show 2 more

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https://doi.org/10.3390/s120303186

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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.

Highlights

Fingerprint segmentation is an important pre-processing step in automatic fingerprint recognition system [1]
Segmentation features are evaluated using the two-level feature evaluation method, and the feature or feature set with good sensor interoperability are selected according to the evaluation results
A two-level evaluation method is proposed to evaluate features and select a feature or feature set for sensor interoperable fingerprint segmentation

Summary

Introduction

Fingerprint segmentation is an important pre-processing step in automatic fingerprint recognition system [1]. A fingerprint image usually consists of two regions: the foreground and the background. The foreground which contains effective ridge information is originated from the contact of a fingertip with the sensor. The noisy area at the borders of the image is called the background. Fingerprint segmentation aims to separate the fingerprint foreground area from the background area. Accurate segmentation is especially important for the reliable extraction of minutiae, and reduces significantly the time of subsequent processing

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Results

Conclusion