A Review on Blood Group Detection Using Fingerprint by Deep Learning

Abstract

Blood group identification is an essential process in medical diagnosis, emergency transfusions, and forensic investigations. Traditional methods for determining blood groups are invasive, time-consuming, and require biochemical testing. This project proposes a non-invasive and efficient approach to predict a person’s blood group using fingerprint patterns through Convolutional Neural Networks (CNNs). Fingerprints contain unique ridge patterns and texture features that may correlate with genetic traits, including blood groups. In this study, a CNN model is trained on a dataset of fingerprint images labeled with corresponding blood groups (A, B, AB, and O). The model automatically extracts relevant features such as ridge flow, bifurcations, and minutiae points to classify the blood group with promising accuracy.

Introduction

This paper reviews fingerprint-based blood group detection methods, with emphasis on machine learning and deep learning techniques. Fingerprints are stable, unique biometric traits formed during fetal development, traditionally used for identification but increasingly studied for their potential to encode biological and genetic information. One such trait is blood group, which is critical for transfusions and medical emergencies. Conventional blood group determination relies on invasive, laboratory-based serological or genetic tests, which are accurate but time-consuming, costly, and impractical in emergencies or resource-limited settings.

Multiple studies across different populations suggest a statistically significant correlation between fingerprint patterns (loops, whorls, arches, and minutiae) and ABO/Rh blood groups, indicating a possible genetic linkage. Early dermatoglyphic research used statistical methods to establish these associations, while more recent work applies image processing, machine learning, and deep learning to automate and improve prediction.

Convolutional Neural Networks (CNNs) have shown particular promise due to their ability to learn complex fingerprint features. Earlier statistical and regression-based models achieved moderate accuracy (~62%), while advanced CNN-based systems combined with ridge extraction and Gabor filtering have reported high accuracy (90–92%), demonstrating the feasibility of a non-invasive, low-cost, and automated alternative to traditional blood testing.

The review also compares existing fingerprint-based approaches, highlighting improvements in accuracy as methods evolved from statistical correlation to deep learning. Despite progress, current clinical systems still rely on invasive blood sampling and laboratory infrastructure, underscoring the need for reliable non-invasive solutions.

Conclusion

The study on blood group detection using fingerprint analysis demonstrates a novel, non-invasive, and cost-effective approach for biological identification. Traditional blood group determination methods rely on chemical and serological tests that require blood samples, reagents, and specialized laboratory facilities, making them time-consuming and sometimes prone to human error. In contrast, the proposed approach leverages the uniqueness of fingerprint ridge patterns to predict an individual’s blood group using advanced digital image processing and deep learning techniques. By integrating Convolutional Neural Networks (CNNs) and other machine learning models, the system provides an automated, efficient, and accurate solution that reduces dependency on laboratory infrastructure while ensuring minimal discomfort to individuals. Furthermore, this approach offers significant advantages in terms of speed, safety, and accessibility. It is particularly suitable for large-scale screening applications, remote or resource-limited environments, and situations where traditional blood sampling may be inconvenient or risky. The results reported in existing studies indicate promising accuracy levels, validating the potential of fingerprint-based blood group prediction as a reliable biometric tool. However, there remains scope for further improvement. Expanding the dataset to include diverse populations, optimizing feature extraction techniques, and incorporating hybrid or ensemble deep learning models could further enhance system performance. In addition, integrating this technology with mobile or web-based platforms may enable real-time applications in healthcare, forensic analysis, and personal identification systems. In conclusion, fingerprint-based blood group detection represents a significant advancement in biomedical and biometric research. It enables rapid, contactless, and reliable blood group prediction by combining modern image processing techniques with artificial intelligence. With continued development and refinement, this approach has the potential to become a widely adopted and dependable tool in medical diagnostics, forensic investigations, and personal identification, marking a transformative step toward non-invasive and intelligent healthcare solutions.

References

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Copyright

Copyright © 2025 Aarsha K S, Arya M A, Nandana T Arjun, Sandra Sadasivan, Gishma K M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.