# Dog Identification Method Based on Muzzle Pattern Image
## 1 Introduction  
The number of animals lost or abandoned in South Korea increases every year. In 2019, about 136,000 lost or abandoned animals were rescued, though only 40% were reunited with their original owner [1]. In an effort to deter abandonment and reduce instances of loss, and improve public health, since 2014 animal registration has been mandatory. As of 2019, 797,000 dogs have been registered under this law. Under the current law, dogs can be registered with invasive or external radio frequency identification (RFID) devices or pet tags. However, the method to inject the invasive RFID device into the epidermis is generally not preferred by the owners because of the concerns about negative side effects and animal welfare issue. Moreover, external RFID devices and pet tags are generally ineffective, as owners often lose the devices or are not aggressive enough about ensuring that their pets are wearing them. In light of what seems to be intractable problems with existing methods, an alternative to invasive and external RFID devices and pet tags are needed. Image-based biometrics could be the solution because biometric information is less likely to be lost and nothing is injected into the dogs. Human biometrics aim to assign a unique identity to an individual according to certain physiological or behavioral characteristics unique to each person [2]. These unique characteristics are based on fingerprints, iris, face, and others [3; 4; 5], which are often called as biometrics modalities,identifiers, or traits. A human biometric system typically consists of four main phases: preprocessing, feature extraction, matching, and postprocessing. The preprocessing stage typically includes image enhancement (e.g., contrast stretching, low pass filtering, etc.). The feature extraction stage typically extracts a unique characteristic identifying an individual, and a variety of feature extraction algorithms have been introduced and applied. The matching stage matches the features extracted from two different images and a proper matching algorithm is selected according to the format of the extracted features. The postprocessing stage typically includes methods to reduce noise and improve results. Adopting human biometrics into animals is a promising technology for the animal identification field. Similar to human biometrics, animal's iris, face, and muzzle pattern are used for individual identification [6; 7; 8; 9; 10]. Among the various biometrics identifiers, muzzle pattern is a special one that can only be applied to certain animals such as cows, dogs, and more. Muzzle pattern has been studied since 1921 [11] and it is considered as an unique animal identifier that is similar to human fingerprints [12]. Recently, several image-based muzzle pattern recognition systems have been proposed and most of them were for individual cattle identification [13; 14; 15; 16; 17; 18]. From the previous studies of cattle muzzle pattern recognition, it has been proved that the well-known general-purpose feature extraction algorithms such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) could be a robust solution for extracting unique features from an individual muzzle pattern images [15; 16; 17; 18]. However, there was no studies answering which algorithm is more appropriate for the muzzle pattern recognition. In addition, while various cattle muzzle pattern recognition methods have been proposed, no study has been conducted for dogs as far as we know. This study has two main purposes. The first is to prove that the proposed methods are effective for dog muzzle pattern recognition. The second is to answer which general-purpose feature extraction algorithm is more appropriate for dog muzzle pattern recognition. To achieve the purposes, a suitable method for dog muzzle pattern recognition based on general-purpose feature extraction algorithms is proposed and evaluated.