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Author(s):

Saranya S.R | S.Jeevanraja | Dinesh.M | Surya.K | Yuvanesh.K
Keywords:

Blood Group Detection, Fingerprint Recognition, Deep Learning, Convolutional Neural Network (CNN), Image Processing, Biometric Identification.
Abstract:
Fingerprint-based Blood Group Detection Is An Innovative And Emerging Technique That Integrates Deep Learning And Image Processing To Predict An Individual's Blood Type From Fingerprint Patterns. This Method Leverages The Correlation Between Fingerprint Ridge Characteristics And Blood Group–related Antigens Secreted Through Sweat Glands. After Obtaining The Fingerprint Image, Preprocessing Techniques Are Applied To Enhance Its Quality, Followed By Feature Extraction Using Convolutional Neural Networks (CNNs) And Other Machine Learning Models. The Trained Model Then Classifies The Fingerprint Into Specific Blood Groups Such As A+, B-, O+, Etc. The Proposed Method Aims To Provide A Non-invasive, Fast, And Portable Alternative To Traditional Serological Blood Tests. This Approach Has Potential Applications In Emergency Healthcare, Forensic Science, And Medical Diagnostics.