Hadid, Abdenour

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Name
Hadid, Abdenour
Main Affiliation
Sorbonne Centre for Artificial Intelligence (SCAI)
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ORCID
0000-0001-9092-735X

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  • Publication
    Face Sketch Synthesis using Generative Adversarial Networks
    (2022)
    Hadid, Abdenour 
    ;
    Mahfoud, Sami
    ;
    Daamouche, Abdelhamid
    ;
    Bengherabi, Messaoud
    ;
    Boutellaa, Elhocine
    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.
      37  19
  • Publication
    Hand-drawn face sketch recognition using rank-level fusion of image quality assessment metrics
    (2022)
    Hadid, Abdenour 
    ;
    Mahfoud, Sami
    ;
    Daamouche, Abdelhamid
    ;
    Bengherabi, Messaoud
    Face Sketch Recognition (FSR) presents a severe challenge to conventional recognition paradigms developed basically to match face photos. This challenge is mainly due to the large texture discrepancy between face sketches, characterized by shape exaggeration, and face photos. In this paper, we propose a training-free synthesized face sketch recognition method based on the rank-level fusion of multiple Image Quality Assessment (IQA) metrics. The advantages of IQA metrics as a recognition engine are combined with the rank level fusion to boost the final recognition accuracy. By integrating multiple IQA metrics into the face sketch recognition framework, the proposed method simultaneously performs face-sketch matching application and evaluates the performance of face sketch synthesis methods. To test the performance of the recognition framework, five synthesized face sketch methods are used to generate sketches from face photos. We use the Borda count approach to fuse four IQA metrics, namely, structured similarity index metric, feature similarity index metric, visual information fidelity and gradient magnitude similarity deviation at the rank-level. Experimental results and comparison with the state-of-the-art methods illustrate the competitiveness of the proposed synthesized face sketch recognition framework.
      103  1