Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

State-of-the-art fingerprint recognition systems perform far from satisfactory on noisy fingerprints. A fingerprint denoising algorithm is designed to eliminate noise from the input fingerprint and output a fingerprint image with improved clarity of ridges and valleys. To alleviate the unavailability of annotated data to train the fingerprint denoising model, state-of-the-art fingerprint denoising models generate synthetically distorted fingerprints and train the fingerprint denoising model on the synthetic data. However, a visible domain shift exists between synthetic training data and the real-world test data. Subsequently, state-of-the-art fingerprint denoising models suffer from poor generalization. To counter this drawback of state-of-the-art, this research proposes to align the synthetic and real fingerprint domains. Experiments conducted on publicly available rural Indian fingerprint demonstrate that after the proposed domain alignment, equal error rate improves from 7.30 to 6.10 on Bozorth matcher and 5.96 to 5.31 on minutiae cylinder code (MCC) matcher. Similar improved fingerprint recognition results are obtained for IIITD-MOLF and private rural fingerprints database as well.