## 2 Related Work  
Even though there is no literature available regarding masked face age estimation (MFAE), this section succinctly reviews the associated works of facial age estimation. Facial age estimation (FAE) has been a trending research topic among computer vision researchers for more than a decade. Similarly, face recognition (FR) also deals with facial features to recognize a person by looking at his/her face. Due to the COVID-19 pandemic, some face recognition research has already expanded to the masked face recognition arena. We try to provide a cursory outline of the way in which these tasks were approached earlier by other researchers. Currently, FR research approaches mainly focus on deep-learning-based solutions to recognize a person's face through significant systems such as DeepFace [8], DeepID [9], ArcFace [10], and CosFace [11]. Although deep-learning-based approaches achieved enviable performance, they are still deficient when dealing with faces in unconstrained settings (e.g., occluded faces). Facial occlusion due to wearing a mask is a challenging problem in the event of face recognition, facial age estimation, etc. The occluded face recognition problem is generally approached by researchers in the following ways: image reconstruction [3], occlusion discarding [4; 5], and deep face recognition. In addition to the above-stated approached, some deep-learning-based approaches [12; 13] address face recognition by utilizing a generative adversarial network and attention-based approach convolutional block attention model [14] to simplify and focus on more discriminative and expressive features. In parallel, academicians also commit their best efforts to creating face-feature-based methods for demographic estimation. A recent survey of age estimation including all approaches from the last few decades can be found in [15]. There are various prominent age estimation models that use different image representation techniques, such as anthropometric models [16], the active appearance model (AAM) [17], active shape models (ASMs) [18], the aging pattern subspace model (AGES) [19], models based on age manifold [20], and appearance models [21]. From another viewpoint, the conventional classification approach is not solely explored as a method for facial age estimation. It can be categorized as either a multi-class classification problem [6], regression problem [22], or hierarchical problem [23; 24] as an integration of classification and regression. The lion's share of age-estimation-based background studies that concern classification [25; 26; 27], regression [28; 29; 30; 31; 32; 33; 34], and hybrid [35; 36; 37; 38; 39] problems are reported in the references. In [19], aging features were extracted from an aging pattern representative subspace, and a robust regressor was used to determine age. In [25], the authors generated a face parametric statistical model and evaluated it with classifiers formulated on a shortest-distance neural network, and used quadratic function to model the correlation between age and face parameters. In [38], local binary pattern (LBP) histograms were used as features for classification. In [29], a multi-level local binary pattern (MLBP) was made up of several single-level LBPs and finally concatenated to extract local and global texture features. [PERSON] et al. [34; 35] introduced locally adjusted robust regression (LARR), where manifold learning was likely for feature extraction, and then, LARR was adopted for age prediction. In the last decade, researchers have changed their direction of study toward deep learning, especially convolutional neural networks (CNNs), which are remarkably effective and promising compared to typical age estimation techniques. These methods can automatically learn different image representations and provide plausible results for demographic estimation [40]. The Deep Learned Ageing (DLA) pattern is a six-layer CNN framework proposed by [41], where Principal Component Analysis (PCA) is applied after CNN feature extraction.