## 2 Related Work
### Real Age Estimation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Model** & \multicolumn{2}{c}{**Strengths**} & \multicolumn{1}{c}{**Weakness**} \\ \hline
[24] & Deep Expectation, Vgg16 and Classification & - & Perform age regression through classification without facial landmarks & - & Do not maintain class-wise data balancing during training \\
[24] & Deep Expectation, Vgg16 and Classification & - & Achieve state of the art performance on real age and winner of LAP challenge 2015 on apparent age estimation & - & Huge noisy data in the proposed dataset \\ \hline
[31] & Aging pattern subspace and classification & - & Reconstruct unseen face image and a complete aging face database is not necessary & - & Highly relies on landmark Detecting algorithms \\  & & - & Performance is comparable to the human observer & - & Limited performance on early childhood faces \\ \hline
[48] & Cumulative attribute space and Support vector regression & - & Cumulative attributes capture the shape and texture features from a human face compared to aging & - & Sensitive in the sense of feature inconsistency and impreciseness \\  & & - & & even if the training data is sparse and imbalanced \\ \hline
[49] & Ranking CNN & - & Estimate age based on the age distinctive feature ordering & - & Slower compared to cumulative attribute space model and less accurate with sparse data \\  & Ranking CNN & - & Estimate age based on the age distinctive feature ordering & - & Slower compared to cumulative attribute space model and less accurate with sparse data \\  & Ranking CNN & - & - & Able to estimate three traits jointly from three dimensions \\
[50] & CCA \& PLS for dimensionality reduction and Regression & - & Implicitly benefited from the gender and race features for age estimation & The methods used for dimension reduction is very sensitive to the identity of the features between sets. \\ \hline
[54] & Attention based ShuffleNet and Hybrid model & - & Perform classification and regression together and used a light-weight network for low end devices & - & Not compact enough to deploy in the embedded devices \\ \hline
[56] & Cycle GAN, CNN & - & Resolve data imbalance and network over-fitting by generating sufficient images using CGAN & - & This image generation seems inefficient for the noisy image dataset \\ \hline
[57] & Group-n encoding, series of binary subproblems, and Decoding & - & Utilize the adjacent ages information as shared class features and decompose as binary subproblems simplifies the age estimation & - & Intra and inter group formation is quite random Encoding and decoding does not have too much impact on the performance compared to compute cost \\  & & - & Cost-sensitive learning helps to reduce the data imbalance & - & Intra and inter group formation is quite random Encoding and decoding does not have too much impact on the performance compared to computing cost \\ \hline
[58] & Ordinal Regression and CNN & - & Transformed to binary subproblems and multiple output CNN maintain the correlations between tasks & - & Low robustness \\  & & - & Proposed an Asian Face Age Dataset with more than 160 k images & - & Annot handle multicollinearity between independent features \\ \hline Ours & Deep first-moment refinement, MTCNN, vgg16 & - & Resolve the data sparsity problem exist in the DEX method and specify the target application where to deploy our model & - & Not trained with enough balance data due to very less images for the child’s and elders \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between top performing age estimation algorithms.