Dog Identification using Soft Biometrics and Neural Networks
This addresses the problem of animal identification for pet owners or researchers, but it is incremental as it applies existing transfer learning and fusion methods to a new domain.
This paper tackles the problem of biometric identification for dogs by using a deep neural network that fuses 'soft' biometrics (e.g., breed, height, gender) with 'hard' biometrics (photographs of the face). The result is an accuracy improvement from 78.09% without soft biometrics to 84.94% with them, and breed classification accuracies of 90.80% and 91.29% on two datasets.
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%.