# Exploring Attributions in Convolutional Neural Networks for Cow Identification
## 1 Introduction  
Cattle identification and the ability to precisely pinpoint the individual among many other animals in the herd are critical to modern livestock management. They enable effective tracking of animal movement, the traceability of products of animal origin, and health monitoring, including surveillance of contagious disease outbreaks. They also enhance optimization of production and performance and improve animal welfare. Traditional identification methods such as ear tags and RFID transponders are susceptible to loss, tagging errors, or fraud, necessitating the need for automated, non-invasive solutions [1]. In response, computer vision and deep learning have emerged as promising tools for animal identification, offering high accuracy and scalability. With the advances in the field of computer vision, animal recognition has undertaken new development through visual biometrics [2, 3]. With the use of machine learning and a set of algorithms for image processing and recognition, researchers have successfully identified several vertebrate species [4, 5] including wildlife [6, 7, 8, 9] and farm animals like sheep [10], pigs [11, 12], and cattle [13, 14, 15]. Particular success was achieved with the cattle recognition process usingdeep learning and a CNN [13; 16; 17]. Researchers went further in the identification of face images and cow face parts [18; 19] and even distinguishing particular cattle [20; 21; 22]. Convolutional neural networks (CNNs) are widely used in image classification [23], object detection [24; 25], and identification [26], because they can automatically learn complex patterns in data. Recent advancements in CNNs have demonstrated strong performance s in biometric recognition across multiple domains, including human face recognition, wildlife monitoring, and livestock identification [27]. However, while CNNs excel at learning distinguishing patterns, their decision-making processes remain largely opaque, raising concerns about their robustness and generalization in real-world farm conditions [28]. These networks operate largely as \"black boxes\", and it is often unclear which parts of an image influence network's predictions [29]. In particular, CNN-based animal recognition models may not solely rely on biometric features but also on environmental cues, leading to potential overfitting to background elements rather than the animal itself. This lack of interpretability becomes a significant limitation, especially when the purpose is to assess the abilities of the model to generalize, with limited data available. It is important to emphasize that the use of computer vision and deep learning is explored for the task of identification. It involves a search process, where the goal is to pinpoint the exact match from a database. The images in the database, in general, are not available during training. For example, when identifying a cow in a farm, the system first creates a database with all cows in the farm. Then, for a new image of a cow, it checks the database to find the closest match. In contrast, the task of recognition compares the input against predefined classes or categories. In general, the model is trained with images that represent these classes. For example, in the task of recognizing a cow in a farm, each cow represents one class, but there can be many images for one cow. The model has seen all classes during training and directly outputs the predicted class. In livestock farming, the benefits of individual recognition include health monitoring, improving welfare, and breeding [30]. The task of re-identification is recognizing an individual across different instances or contexts. It is similar to identification, but does not compare an instance to a database [31]. One of the main challenges in CNN-based animal identification is ensuring that models focus on biometric features (e.g., facial patterns, body structure) rather than on background features [32].