| dc.description.abstract | Dog muzzle prints are one of the important feature for non-contact dog identification. This paper proposes an automated dog nasal pattern identification system using Deep Metric Learning (DML), which is a more intuitive classification method than the typical direct classification method. DML learns to map inputs to a vector space with certain characteristics. In this vector space, different muzzle prints of the same dog cluster with each other, while muzzle prints of different dogs are far away from each other. To measure whether two muzzle images belong to the same dog, one calculates the distance between the two vectors representing these two samples. Deep learning frameworks are quite mature and it is not too difficult to train a neural network. However, the DML domain is a relatively new subfield, and its main differences compare to typical deep learning - loss functions designed for DML - have been introduced in recent years, combined with many different frameworks for deep learning, making it difficult to deploy. This paper compares the performance of four DML loss functions on dog muzzle prints images, and transfers the trained neural networks to the Open Neural Network Exchange (ONNX) neural network meddle format, to simplify the deployment process. In this paper, the regions of the dog nasal prints of the input images are predicted using a semantic segmentation neural network, and the prediction is performed using DML with KNN. In the independent identity recognition experiment, the identification rates of 87.3% for KNN(k=1) and 96.7% for KNN(k=3) were achieved for 30 dogs with 4 samples each that had never been seen during the training period. After automating the whole identification process, the identification rate for the 26 dogs never seen during the training period was 76.9% for KNN(k=1) and 65.4% for KNN(k=3). | en_US |