## 3 Methodology
### SIFT--Scale-Invariant Feature Transform  
To recognize objects that are complex in appearance, such as the muzzle patterns of equine/horses, descriptive and unique features are detected and extracted. This is the purpose of the SIFT detector in the proposed system. SIFT deals with the rotation and scale of the subject from which features are to be extracted using a scale space, thus being dependent on the orientation and scale of the image, which means that if the animal is far away from the camera and the face of the horse is slightly rotated, and the system can capture a non-blurry image of the muzzle, SIFT will be able to extract features at this scale. Although the Yolo object detector is trained in such a way that if there is any occlusion, the detector will not detect the nose part, and this was considered to maintain the maximum features of the nose, if any occlusion is present, the SIFT detector can still extract features from the visible subject area on the image. SIFT also ignores the luminance of the subject. It does so by detecting interest points based on blob detection on interest/keypoints or features. The keypoint for SIFT is an area on the image that is rich in its content such as brightness and color variation, and a certain uniqueness is required in this area for it to be a keypoint, which then can be used for matching; being rich in content should also mean that it is well defined for SIFT to generate a descriptor for this point, and its position in the image should also be well defined. All these are characteristics of the grooves, ridges, and beads present on the muzzle, which are detected by the SIFT detector. Figure 7b shows the keypoints on the muzzle of the horse, whereas Figure 7a shows the original image of the nose, which was detected by the Yolo object detector when the animal was quite close to the camera, and the camera was also able to capture many details of the nose. Note that  
Figure 6: Testing Object Detection Model: in (**a**) are the annotated images of the test set, and in (**b**) are the bounding boxes predicted by the Yolo object detection model trained by transfer learning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(n\).**Samples = 242** & \multicolumn{4}{c}{**Predicted**} \\ \hline  & Face & Neg & Nose & Nose\_ & BGFN \\  & Face & 123 & - & - & - \\  & Neg & - & 8 & - & - & 3 \\ True & Nose & - & - & 99 & - & - \\  & Nos\_ & - & - & - & 8 & - \\  & BGFP & - & 1 & - & - & 242 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion Matrix of Yolo Object Detection Model for test set. the image is converted to a gray color. Nonetheless, not all the features of the nose are detected by SIFT; hence, a Gaussian blur filter with histogram equalization is applied to the image and then the image is passed through the SIFT detector, which results in the image in Figure 7c. Here, we can see that the SIFT detector has detected a significantly large number of keypoints, which are manually highlighted by a rectangle in Figure 7d, which is also the region of interest. The algorithm will automatically use these keypoints along with their descriptors for matching purposes because these keypoints have unique and descriptive features that will be used for matching or identification. Moreover, when the animal further away, or the capturing device has low quality, the following can be seen in Figure 8, where the nose of a horse was detected that was somewhat far away from the camera. We can see that the SIFT detector detected the keypoints before and after preprocessing shown in Figure 8b,c, respectively. Figure 7: Keypoint detection on muzzle of a horse: (**a**) originally detected by [PERSON], (**b**) keypoint detection before processing, (**c**) keypoint detection after processing, and (**d**) region of interest is highlighted, where unique and descriptive features are detected as keypoints. Principle of SIFT  
SIFT is a popular feature detection algorithm that is commonly used in computer vision applications.