## 3 Proposed Methodology
### Feature Representation for Expression Classification
\begin{table}
\begin{tabular}{c c c c c} \hline
**Layer** & **OutputShape** & **ImageSize** & **Parameters** \\ \hline \multicolumn{5}{c}{**Block-1**} \\ \hline Conv2D(3 \(\times\) 3)@32 & \((n,n,32)\) & (96, 96, 32) & 896 \\ \hline BatchNorm & \((n,n,32)\) & (96, 96, 32) & 128 \\ \hline ActivationReLU & \((n,n,32)\) & (96, 96, 32) & 0 \\ \hline Maxpool2D(2 \(\times\) 2) & \((n_{1},n_{1},32)\), \(n_{1}=n/2\) & (48, 48, 32) & 0 \\ \hline Dropout & \((n_{1},n_{1},32)\), \(n_{1}=n/2\) & (48, 48, 32) & 0 \\ \hline \multicolumn{5}{c}{**Block-2**} \\ \hline Conv2D (\(3\times 3\))@32 & \(((3\times 3\times 3)+1)\times 32\) & Conv2D (\(3\times 3\))@96 & \((n_{3},n_{3},96)\) & (12,12,96) & (\((3\times 3\times 64)+1\)) \\ \hline BatchNorm & \((n_{1},n_{1},32)\) & (48,48,32) & \(4\times 32=128\) & BatchNorm & \((n_{3},n_{3},96)\) & (12,12,96) & \(4\times 96=384\) \\ \hline ActivationReLU & \((n_{1},n_{1},32)\) & (48,48,32) & 0 & ActivationReLU & \((n_{3},n_{3},96)\) & (12,12,96) & 0 \\ \hline Maxpool2D (\(2\times\) 2) & \((n_{2},n_{2},32)\) & (24, 24, 32) & 0 & Maxpool2D (\(2\times 2\)) & \((n_{4},n_{4},96)\) & (6,6,96) & 0 \\ \hline Dropout & \((n_{2},n_{2},32)\) & (24, 24, 32) & 0 & Dropout & \((n_{4},n_{4},96)\) & (6,6,96) & 0 \\ \hline \multicolumn{5}{c}{**Block-3**} \\ \hline Conv2D (\(3\times 3\))@64 & \(((3\times 3\times 32)+1)\) & Conv2D (\(3\times 3\))@96 & \(((3\times 3\times 96)+1)\) \\ \((3\times 3)\)@64 & \(((3\times 3)\)@64\()\) & \((3\times 3)\)@96 & \((n_{4},n_{4},96)\) & (6,6,96) & \(((3\times 3\times 96)+1)\) \\ \multicolumn{5}{c}{**Block-6**} \\ \hline Conv2D(\(3\times 3\))@64 & \((n_{2},n_{2},64)\) & (24, 24, 64) & 4 \(\times 64=256\) & BatchNorm & \((n_{4},n_{4},96)\) & (6,6,96) & \(4\times 96=384\) \\ \hline ActivationReLU & \((n_{2},n_{2},32)\) & (24, 24, 64) & 0 & ActivationReLU & \((n_{4},n_{4},96)\) & (6,6,96) & 0 \\ \hline Maxpool2D (\(2\times 2\)) & \((n_{3},n_{3},64)\) & (12, 12, 64) & 0 & Maxpool2D (\(2\times 2\)) & \((n_{5},n_{5},96)\) & (3,3,96) & 0 \\ \hline Dropout & \((n_{3},n_{3},64)\) & (12, 12, 64) & 0 & Dropout & \((n_{5},n_{5},64)\) & (3,3,96) & 0 \\ \hline \multicolumn{5}{c}{**Block-6**} \\ \hline Conv2D(\(3\times 3\))@64 & \((n_{5},n_{5},64)\) & (3,3,64) & & 55,360 \\ \hline BatchNorm & \((n_{5},n_{5},64)\) & (3,3,64) & & 256 \\ \hline ActivationReLU & \((n_{5},n_{5},64)\) & (3,3,64) & & 0 \\ \hline Maxpool2D(2x2) & \((n_{6},n_{6},64)\), \(n_{6}=n_{5}/2\) & (1,1,64) & & 0 \\ \hline Dropout & \((n_{6},n_{6},64)\) & (1,1,64) & & 0 \\ \hline
**Layer** & **Output Shape** & **Image Size** & **Parameter** \\ \hline Flatten & \((1,n_{6}\times n_{6}\times 64)\) & \((1,64)\) & 0 \\ \hline Dense & \((1,256)\) & \((1,256)\) & \((1+64)\times 256\) \\ \hline Batch Normalization & \((1,256)\) & \((1,256)\) & \(1024\) \\ \hline Activation Relu & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dropout & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dense & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dense & \((1,256)\) & \((1,256)\) & \((256+1)\times 256\) \\ \hline Batch Normalization & \((1,256)\) & \((1,256)\) & \(1024\) \\ \hline Activation Relu & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dropout & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dense & \((1,7)\) & \((1,7)\) & \((256+1)\times 7\) \\ \hline \multicolumn{5}{c}{Total parameters for the input image size:} & 310,247 \\ \hline \multicolumn{5}{c}{Total number of trainable parameters:} & 308,455 \\ \hline \multicolumn{5}{c}{Non-trainable parameters:} & 1792 \\ \hline \end{tabular}
\end{table} 
Table 2: The proposed CNN architecture for the input size 96 \(\times\) 96 with layers, output shape, image size, and parameters.