## 2 Related Work  
This literature review aims to examine the current state of research on the use of biometric feature extraction and recognition for livestock face recognition, including the benefits, challenges, and future directions of this approach. Livestock face recognition using biometric feature extraction and recognition techniques has gained significant attention in recent years as a means of improving the traceability, disease control, breeding programs, and overall management and organization of livestock. The use of unique physical characteristics, such as muzzle patterns, for the accurate identification of animals, has shown promising results in several studies. In this context, the use of biometric identification technologies, such as automated detection and feature extraction algorithms, can enhance the accuracy and efficiency of livestock identification. [PERSON] et al. [14] proposed a technique for Arabian horse identification based on the muzzle patterns of the horses. They used a fusion of local binary pattern (LBP) and speed-up-robust-features (SURF) techniques to extract features from horses' nose images. The extracted features were then used for classification using a support vector machine (SVM) classifier, which achieved accuracy of 96%. The authors also applied optimization using a gray-wolf algorithm, which further increased the accuracy to 99%. This study demonstrates the use of muzzle patterns for Arabian horse identification. However, the study also has some limitations. One potential limitation is the issue of scale invariance, which refers to the ability of the identification system to accurately recognize the animal regardless of the size of the image. Some identification techniques, such as LBP and SURF, may not be scale-invariant and are sensitive to changes in the size of the image. This can be a problem when dealing with real-world images, as the size of the animal and the distance of the camera from the animal can vary significantly. To address the issue of scale invariance, researchers have explored the use of techniques such as SIFT [9] or histogram of oriented gradients (HOG), which are designed to be more robust to changes in the scale of the image. Other potential approaches for improving the robustness of animal identification systems include the use of ensemble methods or deep learning techniques, which are effective in a variety of image recognition tasks. In addition, it is important to carefully consider the size and diversity of the training dataset and to optimize the various components of the identification system to ensure the best possible performance. [PERSON] et al. [15] proposed a biometric identification technique for cattle using their muzzle prints. The authors extracted the features of muzzle patterns using Gaussian pyramid levels, with different approaches such as appearance-based feature extraction, texture descriptor-based feature extraction, and representation. They used the technique of chi-square-based matching to match the extracted features for the identification of the animal. The proposed algorithm achieved accuracy of 93.87% for the identification of cattle using their muzzle patterns. This study highlights the potential of using muzzle patterns for cattle identification and demonstrates the effectiveness of the proposed feature extraction and matching techniques. However, the study also has some limitations. The authors used live-captured muzzle images of the cattle face, taking the picture-capturing device too close to the face of the animal, which can be a problem because the animals may become agitated or scared at the sight of a camera or phone and may not be stable enough for the captured image to obtain a clear muzzle. The use of live-captured images for animal identification can be challenging due to the inherent variability in the quality of the images and the potential for the animal to become distressed or agitated.