Deep Learning-Based Fingerprint Blood Type Recognition System: Cross-Disciplinary Exploration of Biometrics and Medical AI

This project uses convolutional neural networks (CNN) to predict blood types from fingerprint images, exploring the potential correlation between biometrics and blood types and attempting to provide new technical ideas for non-invasive blood type testing. While the project demonstrates the application potential of deep learning in biometric analysis, it also has scientific controversies and technical limitations, so its practical application value should be treated with caution.

In the interdisciplinary field of biometric recognition and medical diagnosis, whether fingerprint features are correlated with blood types has long been controversial. Traditional medicine holds that the two belong to different biological dimensions, but folk claims and preliminary studies suggest a statistical correlation. Based on this hypothesis, this project attempts to use deep learning to explore the potential connection between fingerprint images and blood types, with the goal of building a non-invasive and convenient blood type testing technology suitable for areas with scarce medical resources or emergency rescue scenarios.

The project uses a classic convolutional neural network architecture as the core model, leveraging its powerful image processing and feature extraction capabilities to learn blood type-related patterns from local features of fingerprints such as ridge directions and bifurcation points. The system supports the recognition of 8 blood types: A+, A-, B+, B-, AB+, AB-, O+, O- (covering combinations of the ABO system and Rh factor). The model's output layer has 8 neurons corresponding to the probability distribution of each blood type, and the highest probability is taken as the prediction result.

One of the biggest challenges of the project is dataset construction: there is a lack of large-scale public fingerprint data with blood type annotations, and data acquisition and privacy protection have high requirements. In terms of feature engineering, it is necessary to handle fingerprint image preprocessing (enhancement, normalization, alignment) to address image differences under different collection conditions; at the same time, the impact of fingerprint rotation, scaling, deformation and other issues on the model must be considered.

Model training follows a standard process: dividing training/validation/test sets to ensure fair evaluation; using data augmentation methods such as random rotation, cropping, and brightness adjustment to improve generalization ability. Performance evaluation metrics include accuracy, precision, recall, F1 score, and confusion matrix. It is necessary to analyze whether the recognition performance of each blood type is balanced; if the effect of a certain type is poor, the training strategy needs to be adjusted or samples added.

The potential value of this system includes: quickly determining blood types in emergency rescue to shorten screening time; establishing a blood type distribution database in the public health field to assist blood allocation; providing additional biometric information for identity identification in forensic medicine.

The scientific community has controversies about the correlation between fingerprints and blood types: mainstream medicine believes that the two have different genetic mechanisms and no direct physiological connection. Technically, the model's predictive ability may come from data bias or spurious correlation rather than real association; in practical applications, the prediction results are only for reference and cannot replace standard serological testing as a basis for medical diagnosis.

This project is a bold attempt of AI in the field of biometric analysis. Although the scientific hypothesis is controversial, it demonstrates the ability of deep learning to process complex biological signals. In the future, it is necessary to accumulate high-quality datasets, improve algorithms, and at the same time consider scientific reliability, ethical compliance, and privacy protection to ensure that the technology serves human well-being.