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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

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Related Experiment Video

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction.

Zhao Zhang1, Shuxin Liu2, Manhua Liu3

  • 1Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, China.

Pattern Recognition
|December 20, 2022
PubMed
Summary
This summary is machine-generated.

Contactless fingerprint recognition is crucial for hygiene and convenience. This study introduces a novel deep learning method for accurate contactless minutiae extraction, improving both location and direction detection.

Keywords:
00-0199-00Contactless fingerprintDeep convolutional neural networkMinutiae extractionMulti-task learning

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Contactless fingerprint recognition offers enhanced hygiene and convenience compared to traditional methods.
  • Challenges in contactless fingerprint recognition include low ridge-valley contrast and pose variations.
  • Accurate minutiae extraction is vital for automated fingerprint recognition systems.

Purpose of the Study:

  • To develop a robust and accurate method for contactless minutiae extraction.
  • To improve upon existing two-stage minutiae extraction techniques.
  • To leverage deep learning for joint minutiae location and direction computation.

Main Methods:

  • A multi-task fully deep convolutional neural network was proposed for joint minutiae detection and direction regression.
  • The network operates directly on grayscale contactless fingerprints without preprocessing.
  • An attention mechanism was incorporated to focus direction regression on minutiae points, alongside a novel joint loss function.

Main Results:

  • The multi-task learning approach outperformed single-task methods.
  • The algorithm demonstrated superior performance compared to existing minutiae extraction algorithms and commercial software.
  • The method effectively extracts minutiae location and direction maps from raw contactless fingerprints.

Conclusions:

  • The proposed multi-task deep learning method significantly enhances contactless fingerprint recognition accuracy.
  • This approach addresses key challenges of low contrast and pose variance in contactless fingerprints.
  • The method offers a promising solution for secure and convenient personal identification.