A Cost-Effective Method for Blood Group Detection Using Fingerprints

In this latest automated world, it has become more and more essential to keep people in a protected way. This paper proposes an important scheme of blood group detection using a fingerprint. There are five feature extraction used in the fingerprint images and represented using a mathematical model. In this paper, we have increased the efficiency of fingerprint matching by combining GLCM based feature extraction, wavelet feature extraction, laws of texture feature extraction and minutiae feature extraction with Back Propagation Neural Network based matching. Once the feature extraction is collected, a database containing other fingerprints are built the extracted features are compared with the Neural Network Classification with the previously available set of fingerprint images. So Depending on the similarities close that is whichever fingerprint gets the highest match with the given input image, we declare the result of the fingerprint blood group it actually belongs to.