A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

A meme is a visual representation that illustrates a thought or concept. Memes are spreading steadily among people in this era of rapidly expanding social media platforms, and they are becoming increasingly popular forms of expression. In the domain of meme and emotion analysis, the detection of offensives is a crucial task. However, it can be difficult to identify and comprehend the underlying emotion of a meme because its content is multimodal. Additionally, there is a lack of memes datasets that address how offensive a meme is, and the existing ones in this context have a bias towards the dominant labels or categories, leading to an imbalanced training set. In this article, we present a descriptive balanced dataset to help detect the offensive nature of the meme’s content using a proposed multimodal deep learning model. Two deep semantic models, baseline BERT and hateXplain-BERT, are systematically combined with several deep ResNet architectures to estimate the severity of the offensive memes. This process is based on the Meme-Merge collection that we construct from two publicly available datasets. The experimental results demonstrate the model’s effectiveness in classifying offensive memes, achieving F1 scores of 0.7315 and 0.7140 for the baseline datasets and Meme-Merge, respectively. The proposed multimodal deep learning approach also outperformed the baseline model in three meme tasks: metaphor understanding, sentiment understanding, and intention detection.