G3PD for fingerprint segmentation

What is it about?

Fingerprint verification is a widely used authentication method in commercial applications and most fingerprint verification systems rely on minutiae for com- paring two fingerprints. Typical steps of fingerprint image processing include segmentation, orientation field estimation, image enhancement by contextual filtering and minutiae extraction. Additionally, many systems include nowadays a software-based liveness detection module which can e.g. be based on histograms of invariant gradients as a countermeasure against so-called spoof attacks. In this paper, we focus on the fingerprint image segmentation step and we propose a global three-part decomposition (G3PD) method to achieve an accurate extraction of the foreground region.

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Why is it important?

Image segmentation is a fundamental step of image processing. In this article, we build a mathematical model for an inverse problem in imaging, i.e. fingerprint image can be considered as the sum of three parts, namely a cartoon (smoothness), a texture (sparsity in the frequency domain), and small objects (noises). Performance evaluations show that our proposed G3PD method consistently outperforms existing methods in terms of segmenta- tion accuracy.

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