## 2 Related Work
### Real Age Estimation  
Age estimation is a long-studied research topic among computer vision researchers. Most of the researchers considered human age estimation as either a classification or regression problem. In the case of age classification, age is coupled with a specific range or age group. On the other hand, age regression is a single value estimated for a person. However, it is very challenging to estimate an exact age due to diversity in the aging process across different ages [26]. Furthermore, for accurate age estimation, the model needs a huge amount of correctly labeled face data. Many of the early age estimation methods used hand-crafted facial features in the constrained imaging conditions. A survey of such methods was reported in [6] and a recent survey of age estimation including all approaches of the last decades can be found in [27]. A method that extracts the geometric features from the face and calculates ratios among the facial features to estimate the age is presented in [28]. Initially, the face wrinkles are detected and localized, then the size and distances of the facial features are measured and finally the face is classified into different age categories. A similar approach as presented in [28] is proposed in [29] by modeling growth-related shape variations observed in human faces considering anthropometric evidence. This work was limited to a certain age. The abovementioned methods are unsuitable for images in-the-wild due to the necessity of accurate localization of facial features. A couple of subspace methods were introduced in [30; 31], where aging features were extracted from an aging pattern representative subspace and a robust regressor was used to predict the face ages. Although the aforementioned methods achieved excellent performances compared to previous cutting-edge methods, some limitations are exposed by these algorithms. Their system worked well only with frontal and properly aligned images. The algorithms proposed by these researchers are not well suited for practical applications where the input images might be collected in an unconstrained environment. There are some methods where face images are represented using spatially localized facial patches. In [32; 33], the patch distribution was represented by exploiting probabilistic Gaussian mixture models [34]. A robust descriptor was used in place of pixel patches in [32]. Later on, the Hidden-Markov-Model [35] was introduced instead of the Gaussian mixture model (GMM) for representation of face patch distribution [36]. A significant amount of research employed different robust image descriptors as an alternative to the local image intensity patch for the age estimation task. [PERSON] et al. [37] used Gabor filters along with a Fuzzy-LDA classifier where one face belongs to multiple age classes. Similarly, Biologically-Inspired-Features in [38], and Local Binary Patterns (LBP) were presented in [39]. The age estimation problem was considered as a regression problem by [PERSON] et al. [40] or as a classification problem using a quadratic function, shortest distance, and neural network-based classifiers in [41]. The popular regression techniques reported by the researchers are Support Vector Regression (SVR) [42], Partial Least Squares (PLS) [43], Canonical Correlation Analysis (CCA) [44]. Accordingly, Nearest Neighbor (NN) and Support Vector Machines (SVM) [45] are the most used classification techniques. Next, we choose a couple of real age estimation methods to describe those that are most related to our suggested method. [PERSON] et al. [30] presented a learning scheme to draw aging features named manifold learning and utilized SVRs with local adjustment for age prediction, [PERSON] et al. [46] extracted features using boosting algorithms and formed a hierarchical approach for classification between age group and regression inside a group (DIF). [PERSON] et al. [31] introduced an aging pattern subspace (AGES) wherefrom features were extracted and performed regression for age estimation.