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AFD-StackGAN: Automatic Mask Generation Network for Face De-Occlusion Using StackGAN.

Abdul Jabbar1, Xi Li1, Muhammad Assam1

  • 1College of Computer Science, Zhejiang University, Hangzhou 310027, China.

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|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces AFD-StackGAN, an automatic method using stacked Generative Adversarial Networks (GANs) to remove masks from faces without user input. The approach effectively restores occluded facial details while maintaining realism and consistency.

Keywords:
automatic mask removalgenerative adversarial network (GAN)image restoration

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Automatic face de-occlusion is challenging due to the need for precise mask detection and realistic detail synthesis.
  • Existing methods often require user interaction for mask definition, limiting practical applications.

Purpose of the Study:

  • To develop an automated system for detecting and removing occlusions, specifically masks, from facial images.
  • To enhance the realism and structural consistency of de-occluded faces without manual intervention.

Main Methods:

  • A novel approach, Automatic Mask Generation Network for Face De-occlusion Using Stacked Generative Adversarial Networks (AFD-StackGAN), was developed.
  • The method employs a two-stage Generative Adversarial Network (GAN) architecture: a Binary Mask Generation Network and a Face De-occlusion Network.
  • A synthetic dataset was created using CelebA images to train the model for robust mask removal.

Main Results:

  • AFD-StackGAN demonstrated robust and efficient removal of complex mask occlusions from facial images.
  • Experimental results showed superior performance compared to previous image manipulation techniques.
  • Ablation studies confirmed the advantages of auto-defined masks and refiner networks in the generation process.

Conclusions:

  • The proposed AFD-StackGAN effectively automates face de-occlusion, significantly advancing the field.
  • The model successfully synthesizes occluded regions with high fidelity, preserving facial appearance and structural integrity.
  • This research offers a significant step towards practical, user-independent facial image restoration.