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Original Article
Fingerprint-Based Blood Group Prediction Using Deep Learning
Marriya Tabassum1
Dr. Khaja Mahabubullah2
1 Student, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Professor & HOD, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: July-August 2025
Pages: 53-57
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250604009
References
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10. R. G. Kumar et al., “Streamlit-based deep learning interface for biometric prediction,” IEEE Access, vol. 9, pp. 123456–123469, 2021.
11. T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression (PIE) database,” in Proc. 5th Int. Conf. on Automatic Face and Gesture Recognition, 2002, pp. 46–51.
12. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 248–255.
13. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.
14. A. Jain, L. Hong, and R. Bolle, “On-line fingerprint verification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 4, pp. 302–314, 1997.
15. A. Ross, K. Nandakumar, and A. K. Jain, Handbook of Multibiometrics, Springer, 2006.
16. S. Minaee et al., “Deep learning-based image classification: A comprehensive review,” J. of Applied Sciences, vol. 10, no. 6, pp. 2201–2249, 2020.
17. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE CVPR, 2018, pp. 4510–4520.
18. N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” J. of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.
19. R. Ranjan, V. M. Patel, and R. Chellappa, “HyperFace: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 1, pp. 121–135, 2019.
20. A. W. Rawat and M. K. Sah, “Fingerprint biometric-based classification for blood group detection using CNN,” in Proc. Int. Conf. on AI & Data Engineering, 2022, pp. 120–127.
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Advances in Neural Information Processing Systems (NIPS), 2012.
3. F. Chollet, Deep Learning with Python, 2nd ed., Manning Publications, 2021.
4. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
5. A. Paszke et al., “PyTorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems, vol. 32, 2019.
6. M. R. Dey and M. S. Islam, “Blood group detection using fingerprint: A new approach,” in Proc. 5th Int. Conf. on Informatics, Electronics and Vision (ICIEV), 2016, pp. 475–480.
7. S. S. Bhattacharya and A. K. Roy, “A biometric technique for blood group detection using fingerprint analysis,” Int. J. of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 2, pp. 98–102, 2016.
8. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. of the 3rd Int. Conf. on Learning Representations (ICLR), 2015.
9. R. Gonzalez and R. Woods, Digital Image Processing, 4th ed., Pearson Education, 2018.
10. R. G. Kumar et al., “Streamlit-based deep learning interface for biometric prediction,” IEEE Access, vol. 9, pp. 123456–123469, 2021.
11. T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression (PIE) database,” in Proc. 5th Int. Conf. on Automatic Face and Gesture Recognition, 2002, pp. 46–51.
12. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 248–255.
13. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.
14. A. Jain, L. Hong, and R. Bolle, “On-line fingerprint verification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 4, pp. 302–314, 1997.
15. A. Ross, K. Nandakumar, and A. K. Jain, Handbook of Multibiometrics, Springer, 2006.
16. S. Minaee et al., “Deep learning-based image classification: A comprehensive review,” J. of Applied Sciences, vol. 10, no. 6, pp. 2201–2249, 2020.
17. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE CVPR, 2018, pp. 4510–4520.
18. N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” J. of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.
19. R. Ranjan, V. M. Patel, and R. Chellappa, “HyperFace: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 1, pp. 121–135, 2019.
20. A. W. Rawat and M. K. Sah, “Fingerprint biometric-based classification for blood group detection using CNN,” in Proc. Int. Conf. on AI & Data Engineering, 2022, pp. 120–127.
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