Iraqi Journal of Intelligent Computing and Informatics

Masked Face Recognition (MFR) is an increasingly important area in biometric recognition technologies, especially with the widespread use of masks as a result of the COVID-19 pandemic. This development has created new challenges for facial recognition systems due to the partial concealment of basic facial features. This paper aims to provide a comprehensive review of the latest developments in the field, with a focus on deep learning techniques, especially convolutional neural networks (CNNs) and twin networks (Siamese networks), which have played a pivotal role in improving the accuracy of covering face recognition. The paper discusses the most prominent challenges, which include changes in lighting, different facial positions, partial concealment, and the impact of mask types on the performance of systems. It also reviews advanced technologies developed to overcome these challenges, including data enhancement using artificial databases and multimedia methods to improve the ability of systems to generalize. In addition, the paper highlights advance in deep network design, feature extraction techniques, evaluation criteria, and data sets used in this area. Moreover, it reviews the various applications of masked face recognition in the fields of security and medicine, highlighting the growing importance of these systems in light of recurrent health crises and increasing security threats. Finally, the paper focuses on future research trends such as developing more efficient algorithms and integrating multimedia technologies to improve the performance of recognition systems in real-world environments and expand their applications.

In current years, Generative Adversarial Networks (GANs) have grown into a popular and active research domain in artificial intelligence and machine learning. GANs provide an adequate data augmentation method, which helps improve model accuracy by generating realistic synthetic samples, especially for underrepresented classes. This paper reviews various techniques and methods used in training GANs, particularly focusing on their role in addressing dataset imbalance. The paper also discusses the implications of utilizing GANs for improving model generalization, mitigating bias, and reducing over fitting.This paper discusses the training methods for generative adversarial models, highlighting their significance. It over views various model design strategies, algorithms, and recent approaches to enhance training