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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

773
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
773

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Related Experiment Video

Updated: Jun 9, 2025

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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Universal Fingerprint Generation: Controllable Diffusion Model With Multimodal Conditions.

Steven A Grosz, Anil K Jain

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 24, 2024
    PubMed
    Summary
    This summary is machine-generated.

    GenPrint generates realistic synthetic fingerprints with controllable variations, addressing privacy concerns and improving recognition accuracy. This framework enhances existing datasets and performs comparably to real data models.

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    Area of Science:

    • Computer Science
    • Biometrics
    • Artificial Intelligence

    Background:

    • Synthetic data generation for fingerprint recognition is crucial for privacy.
    • Existing methods lack sufficient intra-class variation for realistic fingerprint impressions.
    • Controlling appearance factors in synthetic fingerprints remains a challenge.

    Purpose of the Study:

    • To introduce GenPrint, a novel framework for generating diverse synthetic fingerprint images.
    • To enable controllable generation of fingerprints with consistent identity and varied appearance.
    • To overcome limitations in current synthetic fingerprint generation techniques.

    Main Methods:

    • Utilized latent diffusion models with multimodal (text and image) conditioning.
    • Developed a framework for generating fingerprints with controllable factors: class, acquisition type, sensor, and quality.
    • Trained and evaluated GenPrint on multiple public fingerprint datasets.

    Main Results:

    • GenPrint successfully generates diverse fingerprint images while preserving identity.
    • The framework offers explainable control over appearance factors and generates novel styles from unseen devices.
    • GenPrint-generated images achieve comparable or superior accuracy to real data models and enhance dataset diversity.

    Conclusions:

    • GenPrint offers a powerful solution for generating high-quality, controllable synthetic fingerprints.
    • The framework advances privacy-preserving biometrics and improves fingerprint recognition system performance.
    • GenPrint demonstrates the potential of diffusion models in creating versatile and realistic biometric data.