Blood Group Prediction Using Fingerprint Samples

Harshitha M; Harish T; Kishan P; Likith P; Madan Y

doi:10.64388/IREV9I11-1718042

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Blood Group Prediction Using Fingerprint Samples

Harshitha M Harish T Kishan P Likith P Madan Y

Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Pattern Recognition

DOI: 10.64388/IREV9I11-1718042

Abstract

This project develops a non-invasive system to predict human blood group using fingerprint images and deep learning. Fingerprints from the SOCOFing dataset are preprocessed and classified using a CNN/EfficientNet-B0 model into eight blood groups (A+, A−, B+, B−, AB+, AB−, O+, O−). A Python interface enables users to upload a fingerprint and receive an instant prediction, demonstrating that fingerprint patterns can support fast blood group screening without lab tests.

Keywords

Blood group prediction, CNN, deep learning, EfficientNet-B0, fingerprint recognition, non-invasive, SOCOFing dataset.

References

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[2] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. ICML, 2019, pp. 6105–6114.

[3] S. Shehu, A. Ruiz-Garcia, V. Palade, and A. James, “Sokoto Coventry Fingerprint Dataset (SOCOFing),” arXiv:1807.10609, 2018.

[4] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. ICLR, 2015.

[5] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.

[6] R. Cappelli, M. Ferrara, and D. Maltoni, “Fingerprint indexing based on minutia cylinder-code,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 1051–1057, 2011.

[7] C. Szegedy et al., “Going deeper with convolutions,” in Proc. IEEE CVPR, 2015, pp. 1–9.

How to cite this paper

Harshitha M, Harish T, Kishan P, Likith P, Madan Y "Blood Group Prediction Using Fingerprint Samples" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 3731-3735 https://doi.org/10.64388/IREV9I11-1718042

Harshitha M, Harish T, Kishan P, Likith P, Madan Y "Blood Group Prediction Using Fingerprint Samples" Iconic Research And Engineering Journals, vol. 9, no. 11, May. 2026, doi: https://doi.org/10.64388/IREV9I11-1718042

Harshitha M, Harish T, Kishan P, Likith P, Madan Y (2026). Blood Group Prediction Using Fingerprint Samples. Iconic Research And Engineering Journals, 9(11). doi: https://doi.org/10.64388/IREV9I11-1718042

Harshitha M, Harish T, Kishan P, Likith P, Madan Y "Blood Group Prediction Using Fingerprint Samples" Iconic Research And Engineering Journals, vol. 9, no. 11, May. 2026. Crossref, https://doi.org/10.64388/IREV9I11-1718042

@article{1718042,
      author = {Harshitha M, Harish T, Kishan P, Likith P, Madan Y},
      title = {Blood Group Prediction Using Fingerprint Samples},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {3731-3735},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718042.pdf},
      abstract = {This project develops a non-invasive system to predict human blood group using fingerprint images and deep learning. Fingerprints from the SOCOFing dataset are preprocessed and classified using a CNN/EfficientNet-B0 model into eight blood groups (A+, A−, B+, B−, AB+, AB−, O+, O−). A Python interface enables users to upload a fingerprint and receive an instant prediction, demonstrating that fingerprint patterns can support fast blood group screening without lab tests.},
      keywords = {Blood group prediction, CNN, deep learning, EfficientNet-B0, fingerprint recognition, non-invasive, SOCOFing dataset.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1718042}
  }