A Review on Non-Invasive Blood Group Prediction Using Fingerprint and Image Processing

Abstract

The blood group identification is critical for blood transfusions, organ transplantation, and emergency medical care. Conventional methods rely on invasive blood sampling and laboratory testing, which are time-consuming and resource-dependent. Recent advances in biometrics and artificial intelligence have enabled non-invasive alternatives, particularly fingerprint-based blood group detection. Fingerprint ridge patterns, influenced by genetic factors, exhibit correlations with ABO and Rh classifications. This review systematically analyzes existing approaches ranging from image processing and statistical models to deep learning architectures such as CNN, ResNet, and hybrid multimodal frameworks. Comparative evaluation shows that deep learning models achieve accuracies exceeding 95%, significantly outperforming traditional methods. The paper highlights current trends, challenges in dataset standardization, and future opportunities for explainable AI, multimodal biometrics, and portable healthcare solutions. The findings indicate that fingerprint-driven, AI-powered systems can provide rapid, accurate, and scalable blood group identification, offering strong potential for clinical and forensic applications.