Face Sketch Synthesis using Generative Adversarial Networks

Sami, Mahfoud; Abdelhamid, Daamouche; Messaoud, Bengherabi; Elhocine, Boutellaa; Hadid, Abdenour

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]

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

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 >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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