## 2 Related Work
Manifold learning was employed to form the aging pattern and structure extracted from different layers. Ref. [42] demonstrated better performance in age group classification over the in-the-wild benchmark Adience dataset by using a simple CNN structure. Ref. [27] reviewed texture and appearance descriptors in detail to explain the significance of the fusion of both features to estimate age. Ref. [43] adopted the score fusion of regression and classification models based on real values. Additionally, a general-to-specific transfer learning strategy was applied to avoid the overfitting problem on a small apparent age estimation dataset. Ref. [44] designed an ensemble of CNN models wherein different models were trained for different age groups, and at the end, they were combined to achieve better efficiency. Ref. [45] proposed a three-stage cascaded CNN to deal with unconstrained face images in the order of age group classification, apparent age estimation by considering the mean relative age found from every single group, and an age error correction strategy. Ref. [46] classified multiple binary sub-problems and attained them through ordinal regression problems. Biologically inspired features (BIFs) were used for feature extraction tasks. Ranking of a CNN framework was proposed by [47], where a single image was considered while designing the order of age group sub-networks and ranking all these binary outputs to predict the final age label. Ref. [48] proposed an end-to-end age estimation method, incorporating a novel cumulative hidden layer to reduce sample imbalance problems from neighboring ages. In [49], the authors suggested a CNN model competent to work in an unconstrained environment, where attention CNN predicted the best attention grid and Patch CNN identified high-resolution patches for the task of classification using a multi-layer perceptron. In recent years, Ref. [50] unified the strength of a CNN and an extreme learning machine (ELM) for age estimation and gender classification. Ref. [51] proposed a framework of five cascaded structures for age prediction using gender and race information in the form of Gaussian Process Regression (GPR) instead of linear regression. Ref. [52] designed a conditional multi-task learning system with weak label expansion for real age predictions from the gender of a person. Ocular images captured from smartphones were utilized for age classification in [53; 54], which combined multi-stage learned features in the form of score-level fusion and handcrafted features as the feature-level fusion of facial images. In [55], the authors presented and evaluated an age estimation approach in unconstrained images using facial parts (eyebrows, eyes, nose, and mouth), cropped from the input images using landmarks, to feed a compact multi-stream CNN architecture. A CNN-based architecture for a joint age-gender identification method was proposed by [56]. For the CNN network, Gabor filter responses were used as inputs. Ref. [57] proposed a two-stage approach, where the CNN predicted age and gender by using a modified MobileNet. Ref. [58] proposed a novel method based on attention long short-term memory (ALSTM) combined with a ResNet network for fine-grained age estimation. Ref. [59] introduced a novel end-to-end two-level CNN approach to identify age and gender from unfiltered faces. A lightweight CNN network with a mixed attention mechanism for low end-devices was proposed in [60], where the output layer was fused by the classification and regression approach. Another multi-task learning approach merging classification and regression concepts to fit the age regression model with heterogeneous data with the help of two different techniques for partitioning data towards classification was proposed in [61]. To resolve the problem of data disparity and ensure the generality of the model, a very recent method was proposed by [PERSON] et al. [62], where a cycle-generative-adversarial-network-based race and age image transformation method was used to generate sufficient data for each distribution.