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Thermal-Visible Face Recognition Based on CNN Features and Triple Triplet Configuration for On-the-Move Identity

Marcin Kowalski1, Artur Grudzień1, Krzysztof Mierzejewski2

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This study explores thermal-visible face recognition, combining visible and thermal infrared imaging. A novel triple triplet method achieved up to 90.61% accuracy in face verification, outperforming existing approaches.

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Area of Science:

  • Biometrics and pattern recognition
  • Computer vision
  • Infrared imaging technologies

Background:

  • Visible face recognition is widespread, but underutilizes infrared spectrums.
  • Thermal-visible face recognition offers advantages in low-light conditions and robustness.
  • Existing methods require further exploration for optimal performance.

Purpose of the Study:

  • To investigate thermal-visible face verification using diverse algorithm architectures.
  • To propose and evaluate a novel 'triple triplet' deep learning method.
  • To benchmark performance against established face recognition techniques.

Main Methods:

  • Utilized four distinct algorithm architectures: Siamese, Triplet, Verification Through Identification, and a novel Triple Triplet CNN.
  • Tested algorithms on multiple publicly available thermal-visible face databases.
  • Implemented various configurations for Siamese and Triplet network approaches.

Main Results:

  • The proposed triple triplet method demonstrated superior performance compared to reference methods.
  • Achieved a True Accept Rate (TAR) of 90.61% at a False Accept Rate (FAR) of 1%.
  • Validated the effectiveness of combining multiple CNNs within triplet branches for enhanced verification.

Conclusions:

  • The triple triplet method represents a significant advancement in thermal-visible face verification.
  • This approach effectively leverages complementary information from visible and thermal infrared spectra.
  • Further research into multi-modal biometric systems is warranted for improved security and identification.