## 4 Discussion
It was found by using the subject-inclusive protocol [61]. Same subjects are allowed to appear as test and train samples in subject-inclusive protocol. Similar classification accuracy is reported in [64] as 61.8%. Although the reported accuracy rate is relatively competitive, the work relied on several very deep CNNs (Res-50 DNNs) to extract the feature set and it resulted in high computation time. Our proposed work achieved the highest accuracies in the literature of age and gender classification from unconstrained facial images by following the subject-exclusive protocol. Among our proposed methods, the JFN-A and JFN-SS achieved the highest exact accuracies of 65.55% and 65.20%, respectively. In general, the following points should be considered in order to perform an efficient and successful age estimation:  
* The availability of large databases is essential to perform efficient age classification from facial images. It is especially important when the model utilizes deep and very deep NNs. Otherwise, the model is liable to overfitting problem. Database should contain sufficient examples of reflecting real environment challenges, such as pose, illumination, resolution, and other real conditions. In the case of relatively small databases, transferable learning could be used to \begin{table}
\begin{tabular}{c c c c} \hline  & **Method** & **Exact Accuracy** & **1-Off Accuracy** \\ \hline
[5] & Shallow CNNs Using Single Crop & 49.5 & 84.6 \\ Shallow CNNs Using Over-Sample & 50.7 & 84.7 \\ \hline
[10] & DNN1 with Facial Features & 57.45 & 94.32 \\ DNN2 with Superpixels Features & 53.62 & 81.40 \\ Facial and Superpixels as input for JFN-SS & 62.37 & 94.46 \\ \hline
[35] & Cascaded Convolutional Neural Network & 52.88 & 88.45 \\ \hline
[57] & LBP & 41.4 & 78.2 \\ LBP + FPLBP & 44.5 & 80.7 \\ LBP + FPLBP + Dropout 0.5 & 44.5 & 80.6 \\ LBP + FPLBP + Dropout 0.8 & 45.1 & 79.5 \\ \hline
[58] & Compact DCNN architecture as base for video learning & 42.0 & - \\ \hline
[59] & Employ light weight DCNN for a multitask learning scheme (age & 49.7 & - \\  & + gender), best model single-6-conv & 49.7 & - \\ \hline
[60] & DCNNs based on VGG-16 architecture + softmax expected & 55.6 & 89.7 \\ \hline
[61] & Subject-Exclusive DAPP & 54.9 & - \\  & Subject-Inclusive DAPP & 62.2 & - \\ \hline
[62] & LSDML: w/o data augmentation & 56 & - \\  & LSDML: random cropping + horizontal flipping & 56.9 & - \\  & M-LSDML: w/o data augmentation + 3 DB & 58.2 & - \\  & M-LSDML: random cropping + horizontal flipping + 3 DB & 60.2 & - \\ \hline
[63] & Proposed CNN-ELM + Dropout 0.5 & 51.4 & - \\  & Proposed CNN-ELM + Dropout 0.7 & 52.3 & - \\ \hline
[64] & VGG-16-Faces + Attention Network & 61.8 & 95.1 \\ \hline  & Facial and Superpixels as input for JFN-A & 63.78 & 93.70 \\  & (1) Superpixels Features Concatenated with Their Robust Features & 58.31 & 86.17 \\ This work & (2) Facial Features Concatenated with Their Robust Features & 63.22 & 94.38 \\  & (1) + (2) as input for JFN-A & 65.55 & 94.86 \\  & (1) + (2) as input for JFN-SS & 65.20 & 91.39 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of state-of-the-art results (%). compensate the limited facial images. For example, pre-trained models, which were trained over large databases for different yet related tasks, could be used and customized. * Extracting distinctive feature set(s) is also a very important step. Distinctive features allow for the classifier to differentiate between different age groups efficiently. Finding such feature sets is not an easy task and it needs a thorough investigation and study. * Choosing a classifier to utilize and customize for the age classification task from facial images plays a major role in reaching a satisfactory and competitive classification accuracies.