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Accurate fingerprint segmentation is crucial for reliable fingerprint recognition systems. This paper presents two novel segmentation methods, GMFS and SUFS, inspired by the KISS (Keep It Simple and Straightforward) principle. Both methods, evaluated on a public benchmark and compared to eighteen state-of-the-art approaches, excel in terms of accuracy, while maintaining simplicity and computational efficiency. GMFS utilizes a single handcrafted feature for straightforward yet effective fingerprint segmentation, achieving superior performance compared to previously reported traditional methods. SUFS employs a simplified U-net architecture for end-to-end segmentation, demonstrating remarkable performance: it achieves an average classification error rate of 1.51% across the entire benchmark, with an improvement of over 40% compared to the previously best-performing method. Furthermore, despite being trained on a relatively small dataset, it exhibits significant generalization capabilities, effectively segmenting fingerprints from very different acquisition technologies without requiring fine-tuning. An open-source Python implementation of both methods is available, fostering further research and development in the field of fingerprint recognition.

Unveiling the Power of Simplicity: Two Remarkably Effective Methods for Fingerprint Segmentation