الفهرس 
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Abstract
Abstract
Face inpainting is a currently developing technology with multiple real-life applications. The main objective of this thesis is to enhance an intelligent face inpainting system, which helps in raising the efficiency of face recognition systems. The enhanced system uses Generative Adversarial Network (GAN) in order to inpaint face image. The network consists of a subnet to predict landmarks, and another one to generate a new pixel for missing parts based on the predicted landmarks. To reconstruct an intelligent system capable of face inpainting correctly, the system was trained using two databases, and they are Large-scale CelebFaces Attributes Dataset (CelebA) beside Novel Landmarked Face Database for Arab Celebrities and evaluated on them. The face inpainting system approach consists of four steps as follows:
1. Create new Arab face database.
2. Construct our enhanced face inpainting model.
3. Train the model on two datasets different in ethnicity in order to enhance face landmark guidance and face completion.
4. Inpainting using landmark guided.
iv
Finally, an intelligent system is developed which able to inpaint a complete face image in the correct way. from the quantitative results, the proposed method achieves the maximum score of 34.97, 0.989 and 1.82 on PSNR (Peak Signal to Noise Ratio), SSIM (Structure Similarity Index Measure) and FID (Fréchet Inception Distance) metrics, respectively. This approach is implemented by using python programming language.