## 3 Proposed Method
### Performance Metric  
For the quantitative evaluation of our age and gender estimation experiments, we use different evaluation metrics. Mean Absolute Error (MAE) and Cumulative Score (CS) measures are used for evaluating the age prediction task whereas Accuracy (ACC) is used for the gender estimation task. Besides accuracy, for evaluation of a classification model, two other measures, the positive predictive value (PPV) and the true positive rate (TPR) are considered as a performance metric for our experiments. To illustrate the performance metrics for the binary classification problem, the terms needed to form the equation are summarized in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** &
\begin{tabular}{c} **Feature Map** \\ **Size** \\ \end{tabular} & **Filter Size** & **Stride** & **Parameters** \\ \hline Input & \(224\times 224\times 3\) & & & \\ \hline CONV1-1 & & \(3\times 3\) & \(1\times 1\) & \(1792\) \\ *BR & & & \(128\) \\ CONV1-2 & \(224\times 224\times 64\) & \(3\times 3\) & \(1\times 1\) & \(36,928\) \\ *BR & & & \(128\) \\ \hline MaxPool & \(112\times 112\times 64\) & \(2\times 2\) & \(2\times 2\) & \\ \hline CONV2-1 & & \(3\times 3\) & \(1\times 1\) & \(73,856\) \\ *BR & & & \(256\) \\ CONV2-2 & \(112\times 112\times 128\) & \(3\times 3\) & \(1\times 1\) & \(147,584\) \\ *BR & & & \(256\) \\ \hline MaxPool & \(56\times 56\times 128\) & \(2\times 2\) & \(2\times 2\) & \\ \hline CONV3-1 & & \(3\times 3\) & \(1\times 1\) & \(295,168\) \\ *BR & & & \(512\) \\ CONV3-2 & \(56\times 56\times 256\) & \(3\times 3\) & \(1\times 1\) & \(590,080\) \\ *BR & & & \(512\) \\ \hline MaxPool & \(28\times 28\times 256\) & \(2\times 2\) & \(2\times 2\) & \\ \hline CONV4-1 & & \(3\times 3\) & \(1\times 1\) & \(1,180,160\) \\ *BR & & & \(1024\) \\ CONV4-2 & \(28\times 28\times 512\) & \(3\times 3\) & \(1\times 1\) & \(2,359,808\) \\ *BR & & & \(1024\) \\ \hline MaxPool & \(14\times 14\times 512\) & \(2\times 2\) & \(2\times 2\) & \\ \hline CONV5-1 & & \(3\times 3\) & \(1\times 1\) & \(2,359,808\) \\ *BR & & & \(1024\) \\ CONV5-2 & \(14\times 14\times 512\) & \(3\times 3\) & \(1\times 1\) & \(2,359,808\) \\ *BR & & & \(1024\) \\ \hline MaxPool & \(7\times 7\times 512\) & \(2\times 2\) & \(2\times 2\) & \\ \hline FC1 & & & & \(102,764,544\) \\ Dropout & \(4096\) & & & \(16,781,312\) \\ Dropout & & & & \(4,097,000\) \\ FC3 & \(1000\) & & & \\ Dropout & \(1010\) or 2 & & & \\ \hline FC4 & \(101\) or 2 & & \(101,101\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of our age and gender estimation network. MAE: The estimated age is reported as the mean absolute error (MAE) in years. It computes the mean of the absolute error between the predicted and ground truth age. MAE is considered as the de facto standard for measuring age estimation performance because the lion's share of the literature used it for the model evaluation. The MAE is calculated using Equation (2). \[\text{MAE}=\frac{\sum_{i=1}^{n}\left|y_{i}^{p}-y_{i}^{gt}\right|}{N} \tag{2}\]  
where \(N\) is the number of images that belongs to the test set, \(y_{i}^{gt}\) denotes the ground truth age, and \(y_{i}^{p}\) denotes the predicted age of the \(i\)th image. Cumulative Score (CS): A cumulative score is the number of test images having an absolute error that is no larger than a threshold value \(t\) over the total number of test images. The equation used for calculating the cumulative score is given below:  
\[\text{CS}=\frac{N_{ae<t}}{N}*100\% \tag{3}\]  
where \(t\in[0,100]\) and \(N_{ae<t}\) represents the quantity of images from a test set that possesses an absolute error less than the specified threshold value. The total number of test images are denoted as \(N\). Accuracy (ACC): Accuracy is the number of correct predictions made by the model in relation to its overall prediction. It is a good measure when the target variable classes in the data are nearly balanced. The corresponding formula for accuracy is presented in Equation (4).