## 3 Proposed Methodology
### Factors Affecting the Performance of the Proposed Fers
The classical augmentation techniques that were employed are bilateral filtering, unsharp filtering, horizontal flip, vertical flip, Gaussian blur,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Output Shape** & **Image Size** & **Parameters** \\ \hline \hline \multicolumn{3}{c}{**Block-1**} \\ \hline Conv2D(3 \(\times\) 3)@16 & \((n,n,16)\) & \((192,192,16)\) & 448 \\ \hline BatchNorm & \((n,n,16)\) & \((192,192,16)\) & 64 \\ \hline ActivationReLU & \((n,n,16)\) & \((192,192,16)\) & 0 \\ \hline Maxpool2D(2 \(\times\) 2) & \((n_{1},n_{1},16)\), \(n_{1}=n/2\) & \((96,96,16)\) & 0 \\ \hline Dropout & \((n_{1},n_{1},16)\) & \((96,96,16)\) & 0 \\ \hline Block-2 & \multicolumn{3}{c}{Block-2} \\ \hline Conv2D(3 \(\times\) 3)@32 & \((n_{1},n_{1},32)\) & \((96,96,32)\) & 4640 \\ \hline BatchNorm & \((n_{1},n_{1},32)\) & \((96,96,32)\) & 128 \\ \hline ActivationReLU & \((n_{1},n_{1},32)\) & \((96,96,32)\) & 0 \\ \hline Maxpool2D(2 \(\times\) 2) & \((n_{2},n_{2},32)\), \(n_{2}=n_{1}/2\) & \((48,48,32)\) & 0 \\ \hline Dropout & \((n_{2},n_{2},32)\) & \((48,48,32)\) & 0 \\ \hline
**Layers** & **Output Shape** & **Image Size** & **Parameters** \\ \hline \multicolumn{3}{c}{**Block-3**} \\ \hline Conv2D & \((n_{2},n_{2},32)\) & (48,48,32) & \(((3\times 3\times 3))\)\(+\)\(1\times 32\) & Conv2D & \((n_{4},n_{4},96)\) & \((12,12,96)\) & \(((3\times 3\times 64)+1)\) \\ \multicolumn{3}{c}{(3 \(\times\) 3)@632} & \(=9248\) & \((3\times 3)\)@96 & \((n_{4},n_{4},96)\) & \((12,12,96)\) & \(4\times 96-384\) \\ \hline Batch & \((n_{2},n_{2},32)\) & (48,48,32) & 4 \(\times\) 32 = 128 & Batch & \((n_{4},n_{4},96)\) & \((12,12,96)\) & \(4\times 96-384\) \\ \hline Norm & \((n_{2},n_{2},32)\) & (48,48,32) & 0 & Activation & \((n_{4},n_{4},96)\) & \((12,12,96)\) & 0 \\ \hline Maxpool2D & \((n_{2},n_{3},32)\) & & & Maxpool2D & \((n_{5},n_{5},96)\) & \\ \((2\times 2)\) & \((n_{3}=n_{2}/2\) & (24, 24, 32) & 0 & \((2\times 2)\) & \(n_{5}=n_{4}/2\) & \((6,6,96)\) & 0 \\ \hline Dropout & \((n_{2},n_{3},32)\) & (24, 24, 32) & 0 & Dropout & \((n_{5},n_{5},96)\) & (6,6,96) & 0 \\ \hline \multicolumn{3}{c}{**Block-4**} \\ \hline Conv2D & \((n_{3},n_{3},64)\) & (24, 24, 64) & \((3\times 3\times 32)\) + 1 & Conv2D & \((n_{5},n_{5},96)\) & \(((6\times 3\times 396)+1)\) \\ \multicolumn{3}{c}{(3 \(\times\) 3)@64} & \(\times\) 64 = 18,496 & \((3\times 3)\)@96 & \((n_{5},n_{5},96)\) & \((6,6,96)\) & \((6,6,96)\) \\ \hline Batch & \((n_{3},n_{3},64)\) & (24, 24, 64) & 4 \(\times\) 64 = 256 & Batch & \((n_{5},n_{5},96)\) & \((6,6,96)\) & \(4\times 96=384\) \\ \multicolumn{3}{c}{Norm} \\ \hline Activation ReLU & \((n_{3},n_{3},32)\) & (24, 24, 64) & 0 & Activation & \((n_{5},n_{5},96)\) & (6,6,96) & 0 \\ \hline Maxpool2D & \((n_{4},n_{4},64)\) & \((12,\,12,\,64)\) & 0 & Maxpool2D & \((n_{6},n_{6},n_{6},96)\) & (3,3,96) & 0 \\ \((2\times 2)\) & \(n_{4}=n_{5}/2\) & \((12,\,12,\,64)\) & 0 & \((2\times 2)\) & \(n_{6}=n_{5}/2\) & \((3,3,96)\) & 0 \\ \hline Dropout & \((n_{4},n_{4},64)\) & (12, 12, 64) & 0 & Dropout & \((n_{6},n_{6},64)\) & (3,3,96) & 0 \\ \hline \multicolumn{3}{c}{**Block-7**} \\ \hline \hline \multicolumn{3}{c}{Conv2D(3 \(\times\) 3)@64} & \((n_{6},n_{6},64)\) & (3,3,64) & 55,360 \\ \hline BatchNorm & \((n_{6},n_{6},64)\) & (3,3,64) & 256 \\ \hline ActivationReLU- & \((n_{6},n_{6},64)\) & (3,3,64) & 0 \\ \hline Maxpool2D(2 \(\times\) 2) & \((n_{7},n_{7},64)\), \(n_{7}=n_{6}/2\) & (1,1,64) & 0 \\ \hline Dropout & \((n_{7},n_{7},64)\) & (1,1,64) & 0 \\ \hline
**Layer** & **Output Shape** & **Image Size** & **Parameter** \\ \hline Flatten & \((1,n_{7}\times n_{7}\times 64)\) & \((1,64)\) & 0 \\ \hline Dense & \((1,256)\) & \((1,256)\) & \((1,256)\) & \((1+64)\times 256\) \\ \hline Dense & \((1,256)\) & \((1,256)\) & \((1,256)\) & \(=16,640\) \\ \hline Batch Normalization & \((1,256)\) & \((1,256)\) & \(1024\) \\ \hline Activation Relu & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dropout & \((1,256)\) & \((1,256)\) & \((256+1)\times 256\) \\ \hline Dense & \((1,256)\) & \((1,256)\) & \(=65,792\) \\ \hline Batch Normalization & \((1,256)\) & \((1,256)\) & \(1024\) \\ \hline Activation Relu & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dropout & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dropout & \((1,256)\) & \((1,256)\) & \(0\) \\ \hline Dense & \((1,7)\) & \((1,7)\) & \(=1 additive Gaussian noise, image scale, image cropping, translation, image rotation, shear mapping, image zooming, image filling, and contrast normalization methods from [15] for the purpose of image augmentation.