Kinship verification via ear images: A comparative study

Authors

  • Phuong Quang Luu
    Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam
  • Bay Van Nguyen
    Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam
  • Huy Quoc Nguyen
    Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam

DOI:

10.46223/HCMCOUJS.tech.en.15.1.3683.2025

Keywords:

deep learning; ear biometric; kinship verification

Abstract

Kinship verification is crucial in daily life, especially in the legal field. Nowadays, most kinship verification methods utilize the advantages of human DNA and facial features. However, these methods require a lot of complex procedures, so they are unsuitable for real-time application. Therefore, researchers started to propose other promising biometrics, and the human ear is one of the most potential. The human ear has long been recognized as a robust biometric trait, comparable to others, such as face, iris, and fingerprint. This paper proposes using ear images to identify human kinship based on several well-known deep-learning networks. Moreover, an ear image set is presented to tackle the lack of a kinship-annotated dataset.

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References

Alshazly, H., Linse, C., Barth, E., & Martinetz, T. (2020). Deep convolutional neural networks for unconstrained ear recognition. IEEE Access, 8, 170295-170310. https://doi.org/10.1109/access.2020.3024116

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Received: 23-08-2024
Accepted: 27-09-2024
Published: 13-01-2025

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How to Cite

Luu, P. Q., Nguyen, B. V., & Nguyen, H. Q. (2025). Kinship verification via ear images: A comparative study. HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, 15(1), 47–57. https://doi.org/10.46223/HCMCOUJS.tech.en.15.1.3683.2025

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Copyright (c) 2025 Phuong Quang Luu; Bay Van Nguyen; Huy Quoc Nguyen

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.