Face Sketch Synthesis using Generative ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Face Sketch Synthesis using Generative Adversarial Networks
Author(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]
Conference title :
TAMARICS
City :
Tamanrasset
Country :
Algérie
Start date of the conference :
2022
English keyword(s) :
Face Sketch Synthesis
Face Sketch Recognition
Image Quality Assessment
Generative Adversarial Networks
Face Sketch Recognition
Image Quality Assessment
Generative Adversarial Networks
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :