Fingerprint Orientation Refinement Through Iterative Smoothing

Maponi, Pierluigi; Piergallini, Riccardo; Santarelli, Filippo

doi:10.5121/sipij.2017.8503

Cited by 1 publication

(2 citation statements)

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“…Table II compares GBFOE and SNFOE to the 13 approaches whose results on FOE-STD-1.0 are published on FVC-onGoing [74]. These methods can be categorized into three groups: • Local analysis -Gradient, AntheusOriEx, and AnGaFRIS [21],…”

Section: Resultsmentioning

confidence: 99%

“…Other local estimation methods include slit-based techniques, which analyze the pixel intensities along a set of directions (slits) and choose the best orientation according to some measures [12] [13] [14] [15] [16] [17] [18]. Finally, some local estimation methods apply a set of directional filters in the spatial domain [19] [20] [21], while others work in the frequency domain [22] [23] [24]. These methods rely on local information and can generate noisy outputs when faced with low-quality fingerprints.…”

Section: Related Workmentioning

confidence: 99%

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Exploring the Power of Simplicity: A New State-of-the-Art in Fingerprint Orientation Field Estimation

Cappelli

2024

IEEE Access

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Fingerprint recognition is an important tool for personal identification due to its versatility, user-friendliness, and accuracy. Fingerprint orientation field estimation, a crucial step in fingerprint feature extraction, significantly impacts recognition performance. While numerous methods have been proposed, achieving state-of-the-art accuracy often comes at the cost of increased complexity, hindering practical implementation. This work addresses this challenge by introducing two novel, simple, and efficient fingerprint orientation field estimation methods: GBFOE and SNFOE. Both methods adhere to the KISS (Keep It Simple and Straightforward) principle, achieving remarkable performance on publicly available benchmarks. GBFOE outperforms all local methods and rivals more complex approaches, while SNFOE establishes itself as a new state-of-the-art, achieving the highest accuracy on all datasets in both evaluated benchmarks. Surpassing methods designed specifically for latent fingerprints, SNFOE demonstrates exceptional performance on this challenging task within the evaluated benchmarks, highlighting its generalizability despite not being trained on such data. These results underline the potential of simple and efficient methods in fingerprint orientation field estimation, paving the way for practical and resourceefficient fingerprint recognition systems. An open-source Python implementation of both methods is available, fostering further research and development in this field.INDEX TERMS Fingerprints, Orientation field estimation, KISS principle, FOE benchmarks, NIST SD27.

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Section: Resultsmentioning

confidence: 99%