  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%.
