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  1. Home
  3. Cross-sensor Fingerprint Enhancement Using Adversarial Learning And Edge Loss.
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  3. Cross-sensor Fingerprint Enhancement Using Adversarial Learning And Edge Loss.

Related Experiment Video

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss.

Ashwaq Alotaibi1, Muhammad Hussain1, Hatim AboAlSamh1

  • 1Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)
|September 23, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a deep learning algorithm to enhance fingerprint images captured by different sensors, solving cross-sensor matching issues. The method significantly improves fingerprint quality, outperforming existing techniques.

Keywords:
adversarial learningbiometricscGANcross-sensor fingerprintsdeep learningfingerprint enhancement

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Fingerprint sensor interoperability, or cross-sensor matching, is a challenge due to variations in noise and artifacts from different sensor technologies.
  • Existing methods struggle to effectively enhance fingerprints captured across diverse sensor types.

Purpose of the Study:

  • To develop a novel algorithm for enhancing fingerprint images acquired from different sensors and touch technologies.
  • To address the limitations of current fingerprint enhancement techniques in cross-sensor scenarios.

Main Methods:

  • Formulated fingerprint enhancement as an image-to-image transformation task using a deep encoder-decoder model.
  • Employed both conventional and adversarial learning frameworks, including a conditional Generative Adversarial Network (cGAN).
  • Incorporated edge loss during training, inspired by the ridge patterns in fingerprints, and evaluated using MOLF and FingerPass datasets.

    • Main Results:

    • Achieved effective fingerprint quality enhancement across various sensor types, an area previously under-investigated.
    • Demonstrated superior performance compared to state-of-the-art methods in fingerprint enhancement.
    • Validated results using standard metrics like NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM), alongside a proposed Fingerprint Quality Enhancement Index (FQEI).

    Conclusions:

    • The proposed deep learning-based method effectively enhances fingerprint images, overcoming cross-sensor interoperability challenges.
    • The algorithm offers a significant advancement in fingerprint recognition systems by improving image quality regardless of the enrollment sensor.
    • This work provides a robust solution for improving the reliability of fingerprint matching in diverse real-world applications.