## 2 Related Work
#### Limitations of Related Work  
Table 1 depicts the weaknesses of existing classification approaches. Previous methods have at least one of the following limitations:  
* One potential limitation of using LBP and SURF for animal identification is their sensitivity to changes in the size of the image, leading to issues with scale invariance.
* The proposed system using the Gaussian pyramid with chi-square-based matching technique requires one to be very close to the animal to capture the animal's face, which can cause agitation and fear in the animal.
* The movement of the animal can hinder the performance of the DL model and limit its ability to identify because the movement of the animal will cause motion blur in the images.
* The use of the Gabor filter and LDA for horse face recognition may not be practical due to the difficulty in capturing stable images of the horse's face, which can hinder the performance of the identification system and may not be suitable for use in a natural setting.
* The nose images are derived from surveillance video frames, rather than being directly captured. This means that the accuracy of the recognition may be affected if the quality of the video or the visibility of the nose is poor.
* The sparse neural network is unable to detect small faces in the images. This can be a problem when dealing with real-world images. In contrast to the limitations highlighted above, our proposed model presents an innovative approach to identify the animals more accurately. Our contribution to this study is precisely described as below:
* The proposed system can detect and process both small and large faces and the SIFT algorithm is scale-invariant and thus can extract the features of small faces.
* The system uses real-time data for feature extraction and recognition.
* The proposed system is trained to detect only clear images; in the case of motion blur, the model will not detect the muzzle so that only clear images are obtained.
* The system automates the task of capturing the face of the animal by detecting it from a safe distance.
* The system uses SIFT for feature extraction, which is scale-invariant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Methodology** & **Accuracy \%** & **Limitations** \\ \hline Arabian Horse Identification and & & & Sensitive to changes in the \\ Gender Determination System & Fusion of LBP and SURF, SVM & 99.00 & size of the image, leading to \\ Based on Feature Fusion and Gray & with gray-wolf optimization & & issues with scale invariance. \\ Wolf Optimization [14] & & & \\ Muzzle Point Pattern Based & & & \\ Techniques for Individual Cattle & & & \\ Identification [15] & & & \\ Individual Beer Cattle & & & \\ Identification Using Muzzle & Deep learning-based & & Moving object may \\ Images and Deep Learning & multiple models & & deteriorate accuracy. \\ Techniques [16] & & & \\ A Preliminary Investigation on & & & \\ Horses Recognition Using Facial & Gabor filter with LDA & 95.74 & Moving object may \\ Texture Features [17] & & & deteriorate accuracy. \\ Real-time recognition of cattle & & & The accuracy of recognition \\ using animal biometrics [18] & & & may be affected if the \\ Sparse Neural Network for Horse & & & quality of the video or the \\ Face Detection in a Smart Riding & & & visibility of the nose is poor. \\ Club Biometric System [19] & Sparse neural network, LDA, & & The system is unable to \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison and limitations of previous approaches.