Learning a Robust Minutiae Extractor via an Ensemble of Expert Models

Recognizing the limitations of manual methods in creating ground truth minutiae sets for fingerprint recognition, we propose an ensemble method that leverages the strengths of two well-known SDKs: Innovatrics ANSI&ISO v2.4.10 and Verifinger v12.4, referred to as experts. By combining the predictions of these two expert systems, our method aims to learn a robust ground truth minutiae set, serving as a foundation for training fingerprint-matching models. We introduce four ensemble configurations using union and intersection operations to capture a comprehensive set of minutiae, addressing the limitations inherent in any single extractor. Using the ensemble minutiae ground truth, we trained fingerprint recognition models using two distinct architectures: U-Net and Vision Transformers (ViT). These were evaluated using authentication scores and statistics were computed against a hand-marked ground truth dataset. Our experiment results are encouraging across both architectures, particularly for difficult test sets like NIST SD 302. Training with a ViT architecture led to a 1.59% improvement in matching accuracy, increasing from 92.79% for the model trained on Innovatrics ground truth minutiae to 94.38% for the model trained on the ensemble ground truth. The U-Net architecture performance improvement was 1.74%, from 92.00% to 93.74%. These are both significant improvements from the results obtained from the Innovatrics and Verifinger SDKs. These results demonstrate the effectiveness of ensemble ground truth minutiae in enhancing the performance of fingerprint recognition systems.