# Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
## 1 Introduction  
Cattle, buffalo and cows are the major sources of meat in the food supply chain and their protection has become a vital need. Cattle identification is the process of accurately recognizing individual animals--buffalo and cows--via a unique physical marker or biometric identifier. Cattle identification is beneficial to different stakeholders, including animal producers, food consumers and the food industry [1]. For instance, cattle identification systems contribute to limiting the spread of animal diseases by allowing a better understanding of disease trajectories and therefore effectively managing cattle vaccination programs. In addition, cattle identification helps in limiting cattle losses, reducing the costs of disease destruction, minimizing trade losses and facilitating cattle ownership management in large-scale farms [2, 3]. Conventional buffalo and cow identification methods are divided into three groups--permanent, temporary and electrical identification methods [4]. Traditional identification methods, such astattooing, branding, ear notching, and radio-frequency identification (RFID) tagging, confront several challenges pertaining to their susceptibility to shape deformations, tag losses, fraud, animal-welfare concerns and limited scalability [5; 6]. Furthermore, classic cattle identification methods suffer from installation, operational and security limitations [7]. Therefore, the traditional methods are not sufficiently reliable for cattle identification. The current situation raises the necessity for new cattle identification systems, not only for living cattle but also for animal products [8; 9]. Recently, visual cattle biometrics have become an emerging research topic in computer vision [10]. Biometrics is a technology that is used to recognize humans using psychological or behavioral characteristics in both civilian and forensic applications [11; 12]. Employing biometrics to individual cattle identification is a promising technology that overcomes many traditional identification problems. Several cattle identifiers have been studied such as muzzle print patterns [13], iris patterns [14], retinal vascular patterns [15; 16], facial images [17] and DNA profiles [18]. Cattle muzzle print images display distinct grooves, valleys, and beaded structures, and muzzle print images are considered a unique and time-immutable biometric trait that can identify cattle with similar accuracy as that from human fingerprints [19; 20]. Unlike human biometrics, cattle biometrics have attracted less research attention for two main reasons--the lack of standard benchmarking databases and the lack of common features such as minutiae and singular points in human fingerprints [21]. Most of the research performed in cattle identification has utilized muzzle print images combined with local invariant features such as scale-invariant feature transform (SIFT) [22] and speeded-up robust features (SURF) [23] to discriminate between cattle. To achieve more robustness and accuracy in cattle-biometric-based identification systems, further investigations are needed. Although several approaches have been used for extracting and matching features from muzzle print images, bag-of-visual-words (BoVW) has not yet been investigated for cattle identification. This study contributes to the biometric-based cattle identification domain by exploring the performance of the BoVW paradigm for cattle identification purposes. To build the core of the BoVW model, the study utilizes speeded-up robust features (SURF) [24] and maximally stable extremal regions (MSER) [25] as two scale- and rotation-invariant local feature detectors. Identification accuracy, processing time and number of extracted features are considered the performance metrics. The reported findings from this work open the door to further research on cattle identification that can be extended to other types of animals and other biometric identifiers as well. The rest of this article is organized as follows.