| dc.description.abstract | Dairy cows are an animal whose behavior is difficult to control. It is difficult to capture the ideal angle when shooting images on it, especially local features such as cow faces. In this research, a set of dairy cow biometric identity recognition system based on siamese neural network is designed, which can use a small amount of cow face image data set to train deep learning model to achieve effective identity recognition effect. In this research, Yolov4 can quickly detect and segment the dairy cow face in the image, and then input the image test sample and the trained image sample into the neural network model in the Siamese Neural Network to learn features, and use the feature vector to calculates the Euclidean Distance, and then normalize the Euclidean Distance by sigmoid to obtain the similarity between the two input images, and finally compares the similarity to determine the identity of the dairy cow. The experimental results show that Resnet50-based Siamese Neural Network in our system has a 95.5% recognition rate with a small number of training samples (20 images per cow), which is better than the 70% recognition rate of TOP1 using Resnet50 only. In addition to using Resnet50, we also tries to replace different neural network models in our system experiment to compare the recognition effect. The experimental results show that the recognition rate of replacing with Resnet18 can reach 99.5%, replacing with CNN network model has a recognition rate of 75%, replacing with VGG16 has a recognition rate of 90.5%. This shows that the dairy cow biometric identity recognition system we designed can achieve the best recognition effect in the Siamese Neural Network based on Resnet18 can achieve the best recognition effect, and it can have an advantage in a small number of training samples. | en_US |