# Face Gender and Age Classification Based on Multi-Task, Multi-Instance and Multi-Scale Learning
## 3 Experimental Results
### Facial Gender and Age Recognition  
\(\bullet\) Facial Gender Estimation:  
We evaluated the method based on MORPH-II and Adience databases, in comparison with the state-of-the-art facial gender estimation and age grouping methods. Table 3 lists the comparison results of our method, plain CNN [2], RoR [20] and CNN-ELM [3] for gender estimation. For the Adience database, we directly selected the experimental results of plain CNN, RoR and CNN-ELM. For the MORPH-II database, as plain CNN, RoR and CNN-ELM did not conduct experiments based on this database, we reproduced these methods and took the best results for comparison. The plain CNN uses AlexNet architecture to obtain an average accuracy of 98.7% and 86.8% based on MORPH-II and Adience databases, respectively. For Residual Networks of Residual Networks (RoR), which use the basic block and bottleneck block to construct the training network, the average accuracy using RoR is 99.5% and 92.43% based on MORPH-II and Adience databases, respectively. The CNN-ELM combines Convolutional Neural Networks and the Extreme Learning Machine in a hierarchical fashion, which takes advantage of CNN and ELM; the average accuracy using CNN-ELM is 98.5% and 88.2% based on MORPH-II and Adience databases, respectively. Our method achieves an average accuracy of 99.6% and 93.48% based on MORPH-II and Adience databases, respectively, which is competitive with the methods mentioned above. It should be pointed out that the accuracy of the ROR method is similar to that of our method. However, its network is deeper and more complex and its training time is longer. Comparing DRF with RoR, the training time of DRF is less than one-tenth of the time of RoR and the testing time of DRF is also much less than that of RoR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Features**} & **SVM** & **DRF** \\  & **(Gender/Age)** & **(Gender/Age)** \\ \hline
**MMFL** & **92.35/55.24** & **93.48/63.72** \\ Gabor [10] & 82.61/42.72 & 82.45/48.62 \\ LBP [9] & 84.52/41.47 & 85.06/47.67 \\ BIF [11] & 83.48/44.06 & 83.67/50.61 \\ Plain CNN [2] & 86.83/50.75 & 87.14/55.32 \\ ResNet50 [44] & 88.21/51.58 & 89.84/58.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gender and age classification accuracy (%) of SVM/DRF using different image features.  
Figure 6: Examples of recognition results from MORPH-II and Adience datasets. Top row: results of MORPH-II. Bottom row: results of Adience.  
* Facial age grouping:  
In comparison with the state-of-the-art facial age grouping methods, Table 4 shows the average age grouping accuracy based on MORPH-II and Adience datasets. The plain CNN achieves an average accuracy of 89.15% and 50.7% based on MORPH-II and Adience databases, respectively. RoR achieves an average accuracy of 94.86% and 62.34%, respectively. The CNN-ELM achieves an average accuracy of 92.58% and 52.3%, respectively. Our method achieves an average accuracy of 96.14% and 63.72% based on MORPH-II and Adience databases, respectively. It is shown that the accuracy of DRF is greater than that of the other methods.