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Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, for example, for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, that is, dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. The authors propose a novel segmentation method by global three‐part decomposition (G3PD). On the basis of global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non‐zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared with five state‐of‐the‐art methods on a benchmark which comprises manually marked ground truth segmentation for 10,560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy.

Global variational method for fingerprint segmentation by three‐part decomposition