## 2 Related Work
To overcome these challenges, the proposed system uses alternative methods of automating the process for obtaining images; this method can allow for the capture of images from a distance, reducing the need for close contact with the animal and minimizing the risk of distress. In addition to the challenges of image capture, other factors can impact the accuracy of animal identification systems. These include the complexity and uniqueness of the biometric features used, the quality of the feature extraction and matching algorithms, and the size of the database of reference images. To improve the accuracy and robustness of animal identification systems, it is important to consider all of these factors and to carefully optimize the various components of the system. In conclusion, the study by [PERSON] et al. [15] demonstrates the potential of using muzzle patterns for cattle identification and highlights the importance of considering the quality of the images used for identification. [PERSON] et al. [16] proposed a cattle identification approach based on deep learning techniques. The authors used 59 different deep learning models to identify individual cattle using their muzzle prints and achieved accuracy of 98.7%. This study demonstrates the potential of deep learning for cattle identification and highlights the effectiveness of using muzzle prints as a biometric feature for identification. However, it is challenging to use still muzzle point images for identification when the animal is moving. In such cases, it can be difficult to capture a clear image of the muzzle, which can hinder the performance of the deep learning model. This is a common limitation in animal identification, as animals tend to move frequently and may not always be in a stable position. In conclusion, the study by [PERSON] et al. [16] demonstrates the potential of deep learning for cattle identification but it has a limitation in identifying moving animals because, if the animal is moving, the image may be distorted and lose unique information required for the model to identify the animal. [PERSON] et al. [17] proposed a face recognition system for horses using the Gabor filter and linear discriminant analysis (LDA) for facial texture features. The proposed system achieved identification accuracy of 95.74%. However, using horse faces for identification may not be practical due to the difficulty in capturing stable images of the horse's face. Horses tend to move their heads frequently, which can make it difficult to obtain clear images with sufficient features for LDA to make accurate predictions. The use of computer vision for animal identification should prioritize the comfort and well-being of the animal, and a system that requires the animal to remain still may not be ideal. This study highlights the importance of considering the behavior and characteristics of the animal when developing an identification system. While the proposed system by [PERSON] et al. demonstrated high accuracy, its practicality may be limited due to the difficulty in obtaining stable images of the horse's face. Future research could explore alternative methods for identifying horses that do not require the animal to remain still and are more suitable for use in a natural setting. [PERSON] et al. [18] proposed a real-time system for recognizing cattle using the distinctive patterns found on their muzzles or noses. The system involves capturing video footage of the cattle, cropping the frames to isolate the nose/muzzle region, preprocessing the images to remove any noise or blurriness, converting them to grayscale, extracting features using appearance-based algorithms, and storing the features in a database. During testing, a query image of a nose is taken and its features are extracted using a fisher locality preserving projections (FLLP) extractor, which is then matched against the features in the database. A threshold value is returned, which is compared to a predetermined threshold value to determine whether the recognition of the cattle is successful.