Face Sketch Synthesis using Generative ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Face Sketch Synthesis using Generative Adversarial Networks
Auteur(s) :
Sami, Mahfoud [Auteur]
Abdelhamid, Daamouche [Auteur]
Messaoud, Bengherabi [Auteur]
Elhocine, Boutellaa [Auteur]
Hadid, Abdenour [Auteur]
Sorbonne University Abu Dhabi [SUAD]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Abdelhamid, Daamouche [Auteur]
Messaoud, Bengherabi [Auteur]
Elhocine, Boutellaa [Auteur]
Hadid, Abdenour [Auteur]
Sorbonne University Abu Dhabi [SUAD]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Titre de la manifestation scientifique :
TAMARICS
Ville :
Tamanrasset
Pays :
Algérie
Date de début de la manifestation scientifique :
2022
Mot(s)-clé(s) en anglais :
Face Sketch Synthesis
Face Sketch Recognition
Image Quality Assessment
Generative Adversarial Networks
Face Sketch Recognition
Image Quality Assessment
Generative Adversarial Networks
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
Face Sketch Synthesis is crucial for a wide range of practical applications, including digital entertainment and law enforcement. Recent approaches based on GenerativeAdversarial Networks (GANs) have shown compelling results ...
Lire la suite >Face Sketch Synthesis is crucial for a wide range of practical applications, including digital entertainment and law enforcement. Recent approaches based on GenerativeAdversarial 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 aFace Sketch Synthesis technique using conditional GAN to generate facial sketches from facial photographs named cGAN-FSS. Our cGAN-FSS framework generates high perceptualquality of face sketch synthesis while maintaining high identity recognition accuracy. Image Quality Assessment metrics and Face Recognition experiments confirm our proposedframework’s performs better than the state-of-the-art methods.Lire moins >
Lire la suite >Face Sketch Synthesis is crucial for a wide range of practical applications, including digital entertainment and law enforcement. Recent approaches based on GenerativeAdversarial 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 aFace Sketch Synthesis technique using conditional GAN to generate facial sketches from facial photographs named cGAN-FSS. Our cGAN-FSS framework generates high perceptualquality of face sketch synthesis while maintaining high identity recognition accuracy. Image Quality Assessment metrics and Face Recognition experiments confirm our proposedframework’s performs better than the state-of-the-art methods.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Source :