论文标题
使用软生物识别技术和神经网络识别狗
Dog Identification using Soft Biometrics and Neural Networks
论文作者
论文摘要
本文解决了动物，特别是狗的生物识别识别问题。我们在宠物的照片上应用高级机器学习模型，例如深神经网络，以确定宠物身份。在本文中，我们探讨了使用不同类型的“软”生物特征（例如品种，身高或性别）与“硬”生物识别技术（例如宠物脸的照片）融合的可能性。我们在不同的卷积神经网络上运用转移学习的原则，以创建专门为品种分类设计的网络。对于两个不同的数据集，在区分两个狗品种时，提出的网络能够达到90.80％和91.29％的精度。没有使用“软”生物识别技术，狗的识别率为78.09％，但通过使用决策网络融合“软”生物识别技术，识别率可以达到84.94％的准确性。
This paper addresses the problem of biometric identification of animals, specifically dogs. We apply advanced machine learning models such as deep neural network on the photographs of pets in order to determine the pet identity. In this paper, we explore the possibility of using different types of "soft" biometrics, such as breed, height, or gender, in fusion with "hard" biometrics such as photographs of the pet's face. We apply the principle of transfer learning on different Convolutional Neural Networks, in order to create a network designed specifically for breed classification. The proposed network is able to achieve an accuracy of 90.80% and 91.29% when differentiating between the two dog breeds, for two different datasets. Without the use of "soft" biometrics, the identification rate of dogs is 78.09% but by using a decision network to incorporate "soft" biometrics, the identification rate can achieve an accuracy of 84.94%.
