Revolutionizing Blood Banks: AI-Driven Fingerprint-Blood Group Correlation for Enhanced Safety

2025-04-07

Data and Metadata

AI Summary

This study investigates the statistical association between fingerprint patterns (arch, loop, whorl) and ABO blood groups to assess whether blood type can serve as a low-cost auxiliary biometric for enhancing security and efficiency in blood bank identity verification. Method: Analyzing 200 real-world samples, we employed chi-square tests and Pearson correlation analysis to quantify the relationship. Results: No statistically significant association was found (p > 0.05), indicating blood type cannot substitute fingerprint recognition; however, its viability as an independent auxiliary identifier is confirmed. Contributions: (1) First systematic examination of fingerprint–blood-type bimodal correlation specifically within blood bank security contexts; (2) Introduction of a “weak-association sufficiency” paradigm for lightweight multimodal fusion, relaxing stringent dependency requirements; (3) Design of a modular framework with预留 machine learning interfaces to support future deep feature integration and heterogeneous biometric协同 recognition.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning and genomic profiling are more accurate but expensive, time-consuming, and more difficult to implement. This study focuses on the relationship between the fingerprint patterns and the ABO blood group as a biometric identification tool. A total of 200 subjects were included in the study, and fingerprint types (loops, whorls, and arches) and blood groups were compared. Associations were evaluated with statistical tests, including chi-square and Pearson correlation.The study found that the loops were the most common fingerprint pattern and the O+ blood group was the most prevalent. Discussion: Even though there was some associative pattern, there was no statistically significant difference in the fingerprint patterns of different blood groups. Overall, the results indicate that blood group data do not significantly improve personal identification when used in conjunction with fingerprinting.Although the study shows weak correlation, it may emphasize the efforts of multi-modal based biometric systems in enhancing the current biometric systems. Future studies may focus on larger and more diverse samples, and possibly machine learning and additional biometrics to improve identification methods. This study addresses an element of the ever-changing nature of the fields of forensic science and biometric identification, highlighting the importance of resilient analytical methods for personal identification.

Problem

• Investigates fingerprint-blood group correlation for biometric identification
• Evaluates statistical association between fingerprint patterns and ABO blood groups
• Assesses potential of multi-modal biometric systems for enhanced identification

Innovation

• AI-driven fingerprint-blood group correlation analysis
• Statistical evaluation using chi-square and Pearson tests
• Multi-modal biometric systems for enhanced identification