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Feature Interaction-Based Face De-Morphing Factor Prediction for Restoring Accomplice's Facial Image.

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This study introduces a novel method to restore accomplice face images compromised by face morphing attacks. The technique effectively reconstructs identity information, overcoming limitations of existing face de-morphing methods.

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face de-morphingface morphing attackface recognitionfeature interaction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Face morphing attacks degrade the link between face images and identity, challenging face recognition systems.
  • Current detection methods cannot reconstruct accomplice face images.
  • Existing deep learning de-morphing techniques often neglect inherent morphing factors.

Purpose of the Study:

  • To develop a novel face de-morphing method for restoring accomplice identity information.
  • To address the limitations of existing techniques in reconstructing morphed faces.

Main Methods:

  • A novel face de-morphing method predicting a de-morphing factor.
  • Utilizing a channel-wise attention mechanism for feature interaction.
  • Integrating correlation between morphed and reference images for factor prediction.
  • Mapping images to StyleGAN latent space and using inverse linear interpolation for restoration.

Main Results:

  • The proposed method demonstrates superior performance in restoring accomplice facial images.
  • Achieved improved restoration accuracy and image quality compared to existing methods.
  • Successfully reconstructs identity information lost in morphing attacks.

Conclusions:

  • The novel face de-morphing method effectively restores accomplice identity information.
  • The technique offers enhanced accuracy and image quality in face restoration.
  • This approach advances the field of face morphing attack countermeasures.