## Results
### Jointly Fine-Tuning Robust Feature Sets  
The extracted robust facial and superpixels feature sets are used as input for JFN-A and JFN-SS. The performance of the jointly fine-tuning networks based on the amplified feature sets is evaluated by using the proposed robust feature sets. The network is depicted in Figure 5. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Feature Sets** & **Age Groups** & **0-2** & **4-6** & **8-13** & **15-20** & **25-32** & **38-43** & **48-53** & **60-** & **Accuracy** & **1-Off Acc** \\ \hline Facial & 88.41 & 60.18 & 39.12 & 43.61 & 67.14 & 43.79 & 14.52 & 57.20 & 57.45 & 94.32 \\ Robust-Facial & 82.19 & 69.12 & 39.71 & 12.78 & 76.42 & 19.53 & 6.64 & 38.52 & 53.25 & 81.18 \\ Facial + Robust-Facial & 86.96 & 65.96 & 45.88 & 35.24 & 78.98 & 41.22 & 15.35 & 83.66 & 63.22 & 94.38 \\ Superpixels Features & 86.34 & 58.82 & 34.18 & 15.01 & 80.78 & 11.05 & 10.88 & 53.31 & 53.62 & 81.40 \\ Robust-Superpixels & 78.14 & 52.37 & 36.46 & 20.28 & 67.61 & 17.77 & 17.38 & 57.15 & 49.95 & 78.89 \\ Superpixels + Robust-Superpixels & 87.56 & 56.82 & 43.57 & 31.05 & 78.31 & 24.81 & 29.70 & 60.96 & 58.31 & 86.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall classification accuracies for facial robust features on Adience database (%). Appendix A presents the confusion matrices for the robust facial and robust superpixels features. Figure 4: The effect of \(\lambda\) on the accuracy results achieved by the robust features. The best accuracy result is achieved with \(\lambda\) = 0.1 for both feature sets, while the classification accuracy drops when the \(\lambda\) value drifts away from 0.1 from both sides. The network settings for Part 1 and Part 2 of the JFN-A are chosen as two hidden layers of size 1024 and input layer size is 4096 and 512, respectively. The learning rate, dropout rate, and weight decay are 0.1, 0.7, and \(10^{-3}\), respectively, for both parts. Both parts are jointly fine-tuned by using Part 3. Two more hidden layers of size 512 are added on the top of the last hidden layers of Part 1 and Part 2. Then, one output layer with eight age labels is added. The learning rate for Part 3 is set to 0.01, with dropout rate of 0.8, and weight decay of \(10^{-4}\). The training is stopped when there is no improvement in the validation set results. The performance of the second joint fine-tuning method [10] and the proposed robust feature sets are evaluated. The two feature sets that are concatenated with their robust features are trained and tested, as shown in Figure 6.  
The JFN-A and JFN-SS with the two feature sets concatenated with their robust features outperform the accuracies that are observed by using each feature set alone. The results are given in Table 2. The performance of the networks that are using the facial features concatenated with their robust features is better than that of the superpixels features and their robust features for some classes for the same reasons that are explained in the previous section. Figure 5: JFN-A is fed with facial and superpixels feature sets concatenated with their robust features. The facial features that are concatenated with their corresponding robust features are fed to the first part of the network, while the superpixels features and their corresponding robust features are fed to the second part of the network. Figure 6: The proposed cost function with facial and superpixels and their robust features as input. The proposed jointly fine-tuned networks by using the proposed robust features enhanced the overall accuracy in the age estimation. Moreover, it is noticed that both JFN-A and JFN-SS achieve significant results. One-off accuracy of the JFN-A is about 3% higher than that of the JFN-SS. This might be due to the nature of the sigmoid function that is used in the JFN-SS. The sigmoid cost function is mostly used in binary classification and focuses on the target label. It does not care about how close the other labels to the target label. The JFN-A uses the softmax cost function only.