# Dog Identification Method Based on Muzzle Pattern Image
## 4 Results and Discussion
### Evaluation of the Robustness of the Proposed Method  
In order to compare the robustness of the final proposed method, dog muzzle pattern image matching was performed with the augmented test dataset including the deformed images. The test dataset had 4 classes of image deformation: (1) rotation (\(-15^{\circ}\), \(-10^{\circ}\), \(-5^{\circ}\), \(-15^{\circ}\), \(-10^{\circ}\), \(-5^{\circ}\)), (2) intensity \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Size (Pixels)**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Genuine**} & \multicolumn{2}{c}{**Imposter**} & \multicolumn{2}{c}{**GAP**} \\  & & & **Average** & **Min** & **Average** & **Max** & **(Min-Max)** \\ \hline \multirow{8}{*}{Fixed size} & SIFT & 146 & 7 & 4 & 6 & 1 \\  & SURF & 44 & 3 & 4 & 6 & \(-3\) \\  & BRISK & 212 & 3 & 5 & 33 & \(-30\) \\  & ORB & 529 & 4 & 7 & 36 & \(-32\) \\ \cline{2-8}  & SIFT & 168 & 4 & 4 & 6 & \(-2\) \\  & SURF & 63 & 2 & 4 & 7 & \(-5\) \\  & BRISK & 289 & 4 & 5 & 24 & \(-20\) \\  & ORB & 680 & 27 & 7 & 39 & \(-12\) \\ \cline{2-8}  & SIFT & 180 & 6 & 4 & 6 & \(0\) \\  & SURF & 79 & 3 & 3 & 6 & \(-3\) \\  & BRISK & 435 & 2 & 5 & 35 & \(-33\) \\  & ORB & 719 & 24 & 6 & 28 & \(-4\) \\ \hline \multirow{8}{*}{Ratio of original size (Proposed method)} & SIFT & 163 & 4 & 4 & 6 & \(-2\) \\  & SURF & 54 & 3 & 4 & 6 & \(-3\) \\ \cline{1-1}  & BRISK & 249 & 21 & 6 & 28 & \(-7\) \\ \cline{1-1}  & ORB & 600 & 81 & 7 & 34 & 47 \\ \cline{1-1} \cline{2-8}  & SIFT & 189 & 8 & 4 & 8 & \(1\) \\ \cline{1-1}  & SURF & 76 & 4 & 3 & 6 & \(-2\) \\ \cline{1-1}  & BRISK & 346 & 9 & 5 & 32 & \(-17\) \\ \cline{1-1}  & ORB & 725 & 82 & 6 & 25 & \(57\) \\ \cline{1-1} \cline{2-8}  & SIFT & 193 & 6 & 4 & 7 & \(-1\) \\ \cline{1-1}  & SURF & 93 & 4 & 3 & 6 & \(-2\) \\ \cline{1-1}  & SURF & 93 & 4 & 3 & 6 & \(-2\) \\ \cline{1-1}  & SURF & 479 & 7 & 5 & 22 & \(-15\) \\ \cline{1-1}  & ORB & 765 & 48 & 6 & 24 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of good matches according to the resize techniques. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time** **(ms)**} & \multicolumn{2}{c}{**SIFT**} & \multicolumn{2}{c}{**SURF**} & \multicolumn{2}{c}{**BRISK**} & \multicolumn{2}{c}{**ORB**} \\ \cline{2-9}  & **Genuine** & **Imposter** & **Genuine** & **Imposter** & **Genuine** & **Imposter** & **Genuine** & **Imposter** \\ \hline Min & 213 & 194 & 65 & 68 & 555 & 530 & 172 & 108 \\ Max & 647 & 514 & 232 & 193 & 1,400 & 790 & 3,328 & 210 \\ Average & 348 & 306 & 99 & 116 & 751 & 639 & 834 & 159 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The processing time for the proposed methods. change (25, 50, 75, 100), (3) perspective change (Up, Down, Left, Right), (4) noise (salt and pepper, Gaussian blur, vertical motion blur, horizontal motion blur). Those deformations were chosen by taking into account the factors that may occur during the dog muzzle image acquisition. The evaluation of the robustness was performed by adding each class of the deformed images to the original test dataset. For the evaluation of robustness for the image rotation, 330 rotation images and 55 original test dataset images were used. The number of genuine matching was 595 and the number of imposter matchings was 67,375. For the evaluation of the robustness for the intensity, perspective, and noise, 220 images of each deformation class and 55 original test dataset images were used, respectively. The number of genuine matchings was 190 and the number of imposter matchings was 22,000 for each deformation class, respectively. Lastly, the dog identification performance was evaluated with the original data and all the deformed data. The number of genuine matchings was 49,115 and the number of imposter matchings was 496,375. Table 4 shows the result of the robustness comparison of four feature extraction algorithms. For the rotated images, ORB was performed the best and the EER was 0.1%. For the intensity change, SIFT was the best and the EER was 0%.