# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.3 Deep Learning-Based Masked Face Approaches
Comprising 10 learned layers, the DeepMasknet model demonstrated effectiveness in both face mask recognition and masked facial recognition tasks. Furthermore, the authors curated a large and diverse unified dataset, termed the Mask Detection and Masked Facial Recognition (MDMFR) dataset, to evaluate the performance of these methods comprehensively. Experimental results conducted across multiple datasets, including the challenging cross-dataset setting, highlight the superior performance of the DeepMasknet framework compared to contemporary models. [PERSON] et al. [32] proposed a methodology that leverages a fusion of deep learning techniques and Local Binary Pattern (LBP) features for recognizing masked faces. They employed RetinaFace, a face detector capable of handling faces of varying scales through joint extra-supervised and self-supervised multi-task learning, as an efficient encoder. Moreover, the authors extracted LBP features from specific regions of the masked face, including the eyes, forehead, and eyebrows, and integrated them with features learned from RetinaFace within a unified framework for masked face recognition. Additionally, they curated a dataset named COMASK20 comprising data from 300 subjects. Evaluation conducted on both the published Essex dataset and their self-collected COMASK20 dataset demonstrated notable improvements, with recognition results achieving an 87% F1-Score on COMASK20 and a 98% F1-Score on the Essex dataset. [PERSON] et al. [106] introduced a robust masked face recognition system, leveraging the FaceMaskNet-21 deep learning network and employing deep metric learning techniques. Through the generation of 128-dimensional encodings, the system achieves precise recognition from static images, live video feeds, and video recordings in real time. With testing accuracy reaching 88.92% and execution times under 10 ms, the system demonstrates high efficiency suitable for a variety of applications. Its effectiveness in real-world scenarios, such as CCTV surveillance in public areas and access control in secure environments, positions it as a valuable asset for bolstering security measures amid the widespread adoption of face masks during the COVID-19 pandemic. [PERSON] and [PERSON] [104] explored the implications of face masks on the efficacy of face recognition methods, with a specific emphasis on face identification employing deep learning frameworks. Drawing from a tailored dataset derived from VGGFace2 and augmented with masks for 65 subjects, the research scrutinized the performance of prevalent pre-trained models like VGG16 and InceptionV3 after re-training on the masked dataset. Additionally, the study introduced a novel model termed RggNet, which capitalizes on a modified version of the ResNet architecture. This adaptation integrates supplementary layers within the shortcut paths of basic ResNet blocks, mirroring the structure of fundamental VGG blocks. This modification enables the model to effectively grasp an identity function, thereby fostering enhanced feature comprehension across layers. The proposed RggNet model architecture encompasses three sub-blocks organized akin to ResNet50v2, with customized identity blocks featuring convolution layers in lieu of direct shortcuts. Through meticulous experimental analysis, the study endeavored to offer valuable insights into bolstering masked face identification tasks amid the prevalent use of face masks in everyday contexts. [PERSON] and [PERSON] [48] introduced an innovative approach to MFR utilizing deep learning methodologies, notably the convolutional block attention module (CBAM) and angular margin ArcFace loss. By prioritizing the extraction of critical facial features, particularly around the eyes, essential for MFR tasks, their method effectively addressed the challenges posed by facial masks. To mitigate data scarcity, data augmentation techniques were employed to generate masked face images from traditional face recognition datasets.