Face Sketch Synthesis using Generative Adversarial Networks - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Face Sketch Synthesis using Generative Adversarial Networks

Mahfoud Sami
  • Fonction : Auteur
Daamouche Abdelhamid
  • Fonction : Auteur
Bengherabi Messaoud
  • Fonction : Auteur
Boutellaa Elhocine
  • Fonction : Auteur

Résumé

Face Sketch Synthesis is crucial for a wide range of practical applications, including digital entertainment and law enforcement. Recent approaches based on Generative Adversarial Networks (GANs) have shown compelling results in image-to-image translation as well as face photo-sketch synthesis. However, these methods still have considerable limitations as some noise appears in synthesized sketches which leads to poor perceptual quality and poor preserving fidelity. To tackle this issue, in this paper, we propose a Face Sketch Synthesis technique using conditional GAN to generate facial sketches from facial photographs named cGAN-FSS. Our cGAN-FSS framework generates high perceptual quality of face sketch synthesis while maintaining high identity recognition accuracy. Image Quality Assessment metrics and Face Recognition experiments confirm our proposed framework’s performs better than the state-of-the-art methods.
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Dates et versions

hal-03957088 , version 1 (26-01-2023)

Identifiants

  • HAL Id : hal-03957088 , version 1

Citer

Mahfoud Sami, Daamouche Abdelhamid, Bengherabi Messaoud, Boutellaa Elhocine, Abdenour Hadid. Face Sketch Synthesis using Generative Adversarial Networks. TAMARICS, 2022, Tamanrasset, Algeria. ⟨hal-03957088⟩
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