Dog Identification using Soft Biometrics and Neural Networks

• URL: http://arxiv.org/abs/2007.11986v1
• Date: Wed, 22 Jul 2020 10:22:46 GMT
• Title: Dog Identification using Soft Biometrics and Neural Networks
• Authors: Kenneth Lai, Xinyuan Tu, and Svetlana Yanushkevich
• Abstract summary: We apply advanced machine learning models such as deep neural network on the photographs of pets in order to determine the pet identity. 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. The proposed network is able to achieve an accuracy of 90.80% and 91.29% when differentiating between the two dog breeds.
• Score: 1.2922946578413577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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