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Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

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Abstract

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.

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Acknowledgements

The authors acknowledge the support of the HPC services of Inria Sophia Antipolis and IIT Delhi for the computational infrastructure. The authors sincerely thank Prof. Phalguni Gupta from IIT Kanpur and Prof. Kamlesh Tiwari from BITS Pilani for sharing the private rural Indian fingerprint used in this research. The authors acknowledge Naman Mukund from Tekie for his technical guidance.

Funding

This work is partly supported by the French government, the National Research Agency (ANR) under grant ANR-18-CE92-0024, project RESPECT.

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Authors and Affiliations

  1. Inria Sophia Antipolis, Valbonne, France

    Indu Joshi & Antitza Dantcheva

  2. Indian Institute of Technology, Delhi, India

    Indu Joshi, Sumantra Dutta Roy & Prem Kumar Kalra

  3. Delhi Technological University, Delhi, India

    Tushar Prakash & Rohit Kumar

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  1. Indu Joshi
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  2. Tushar Prakash
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  4. Antitza Dantcheva
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  5. Sumantra Dutta Roy
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Correspondence to Indu Joshi.

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A. Dantcheva, one of the co-authors of this manuscript is a member of the editorial board of this journal.

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Joshi, I., Prakash, T., Kumar, R. et al. Unsupervised domain alignment of fingerprint denoising models using pseudo annotations. Multimed Tools Appl 83, 38167–38192 (2024). https://doi.org/10.1007/s11042-023-15513-8

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