Blood group detection using fingerprint

The project "Finger Print Based Blood Group Detection" aims to develop an innovative system that utilizes thumb impressions to identify blood groups. By employing deep learning techniques, specifically Convolutional Neural Networks (CNN) and advanced models such as ResNet combined with Recurrent Neural Networks (RNN) and MobileNet and vision transformer, this system enhances accuracy and efficiency in blood group detection. The implementation leverages Flask or Django for the backend, supported by a user-friendly frontend built with HTML, CSS, and JavaScript. As blood group identification is critical in various medical scenarios, this approach not only streamlines the detection process but also offers a non-invasive alternative to traditional methods. The project aspires to contribute significantly to the fields of medical diagnostics and biometric identification.

KEYWORDS: Fingerprint detection, blood group identification, deep learning, CNN, ResNet, RNN, MobileNet, Flask, Django, biometric analysis.