Dog nose-print identification using deep neural networks

Han Byeol Bae, Daehyun Pak, Sangyoun Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of single-person households. This trend has resulted in a growing interest in problems requiring solutions, such as missing pets and false insurance claims. Traditional non-biometric-based methods cannot address these problems. This paper proposes a novel deep-learning model that can extract discriminative features through dog nose-print patterns for individual identification. We present a robust baseline for how individual dogs can be identified. The proposed dog nose network (DNNet) is a convolutional neural network (CNN)-based Siamese network structure comprising feature extraction and self-attention modules. Moreover, there is no need for a separate scanning device because it uses popular mobile devices to acquire the dataset. Besides high recognition performance, the proposed method also ensures simplicity and efficiency. The proposed method achieves better recognition performance than state-of-the-art methods for the collected dog nose-print dataset. It achieves recognition performance superior to state-of-the-art methods for the collected dog nose-print dataset. Using multiple datasets through cross-validation, we acquired an average identification accuracy of 98.972% with the Rank-1 approach. Additional performance benefits were demonstrated through the receiver operating characteristic (ROC) curve, t-distributed stochastic neighbor embedding (t-SNE), and confusion matrix.

Original languageEnglish
Article number3068517
Pages (from-to)49141-49153
Number of pages13
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported by the Research and Development Program for Advanced Integrated-Intelligence for Identification (AIID) through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT under Grant NRF-2018M3E3A1057289.

Publisher Copyright:
© 2021 American Institute of Physics Inc.. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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