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A multiple datasets deep learning approach for kinship recognition from ear images Cover

A multiple datasets deep learning approach for kinship recognition from ear images

Open Access
|Dec 2025

References

  1. X. Wu et al., “Facial Kinship Verification: A Comprehensive Review and Outlook,” Int J Comput Vis, vol. 130, no. 6, pp. 1494–1525, June 2022, doi: 10.1007/s11263-022-01605-9.
  2. A. Abaza, A. Ross, C. Hebert, M. A. F. Harrison, and M. S. Nixon, “A survey on ear biometrics,” ACM Comput. Surv., vol. 45, no. 2, p. 22:1-22:35, Mar. 2013, doi: 10.1145/2431211.2431221.
  3. Ž. Emeršič, V. Štruc, and P. Peer, “Ear recognition: More than a survey,” Neurocomputing, vol. 255, pp. 26–39, Sept. 2017, doi: 10.1016/j.neucom.2016.08.139.
  4. A. Benzaoui, Y. Khaldi, R. Bouaouina, N. Amrouni, H. Alshazly, and A. Ouahabi, “A Comprehensive survey on ear recognition: Databases, approaches, comparative analysis, and open challenges,” Neurocomputing, vol. 537, pp. 236–270, June 2023, doi: 10.1016/j.neucom.2023.03.040.
  5. Ž. Emeršič, B. Meden, P. Peer, and V. Štruc, “Evaluation and analysis of ear recognition models: performance, complexity and resource requirements,” Neural Comput & Applic, vol. 32, no. 20, pp. 15785–15800, Oct. 2020, doi: 10.1007/s00521-018-3530-1.
  6. D. Meng, M. Nixon, and S. Mahmoodi, “Gender and Kinship by Model-Based Ear Biometrics,” 2019 International Conference of the Biometrics Special Interest Group (BIOSIG), July 2019, Accessed: Nov. 17, 2025. [Online]. Available: https://www.semanticscholar.org/paper/Gender-and-Kinship-by-Model-Based-Ear-Biometrics-Meng-Nixon/8968e30050d9562ee429dfd60a8e512b22a1902f
  7. G. Dvoršak, A. Dwivedi, V. Štruc, P. Peer, and Ž. Emeršič, “Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models,” in 2022 International Workshop on Biometrics and Forensics (IWBF), Apr. 2022, pp. 1–6. doi: 10.1109/IWBF55382.2022.9794555.
  8. P. Q. Luu, B. V. Nguyen, and H. Q. Nguyen, “Kinship verification via ear images: A comparative study,” Ho Chi Minh City Open University Journal of Science – Engineering and Technology pp. 47–57, Jan. 2025, doi: 10.46223/HCMCOUJS.tech.en.15.1.3683.2025.
  9. V. T. Hoang, “EarVN1.0: A new large-scale ear images dataset in the wild,” Data in Brief, vol. 27, p. 104630, Dec. 2019, doi: 10.1016/j.dib.2019.104630.
  10. T.-T. Cao, H.-T. Duong, V.-T. Le, H. Trung, V. Hoang, and K. Tran-Trung, “Enhanced Kinship Verification through Ear Images: A Comparative Study of CNNs, Attention Mechanisms, and MLP Mixer Models,” CMC, vol. 83, no. 3, pp. 4373–4391, 2025, doi: 10.32604/cmc.2025.061583.
  11. J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, “Signature verification using a ‘Siamese’ time delay neural network,” in Proceedings of the 7th International Conference on Neural Information Processing Systems, in NIPS’93. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., Nov. 1993, pp. 737–744.
  12. J. P. Robinson, M. Shao, and Y. Fu, “Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making.” Accessed: Nov. 17, 2025. [Online]. Available: https://www.computer.org/csdl/journal/tp/2022/08/09367013/1rDQQTlFWAU
  13. J. Terven, D. M. Cordova-Esparza, A. Ramirez-Pedraza, E. A. Chavez-Urbiola, and J. A. Romero-Gonzalez, “Loss Functions and Metrics in Deep Learning,” Artif Intell Rev, vol. 58, no. 7, p. 195, Apr. 2025, doi: 10.1007/s10462-025-11198-7.
  14. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Apr. 10, 2015, arXiv: arXiv:1409.1556. doi: 10.48550/arXiv.1409.1556.
  15. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 10, 2015, arXiv: arXiv:1512.03385. doi: 10.48550/arXiv.1512.03385.
  16. Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A ConvNet for the 2020s,” Mar. 02, 2022, arXiv: arXiv:2201.03545. doi: 10.48550/arXiv.2201.03545.
  17. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman, “VGGFace2: A dataset for recognising faces across pose and age,” In: 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), https://www.robots.ox.ac.uk/~vgg/publications/2018/Cao18/
  18. O. M. Parkhi, A. Vedaldi, A. Zisseman, “Deep face recognition”, In: BMVC, Proceedings of the British Machine Vision Conference 2015, British Machine Vision Association, https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
DOI: https://doi.org/10.2478/jee-2025-0058 | Journal eISSN: 1339-309X | Journal ISSN: 1335-3632
Language: English
Page range: 557 - 564
Submitted on: Sep 12, 2025
Published on: Dec 6, 2025
Published by: Slovak University of Technology in Bratislava
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2025 Veronika Kurilova, Martin Bartos, Milos Oravec, Jarmila Pavlovicova, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.