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

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 resource-efficient fingerprint recognition systems. An open-source Python implementation of both methods is available, fostering further research and development in this field.