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@article{169056,
        author = {Siddhi Rakshe and Ravindra Borhade and Bhavesh Pawar and Abhijeet Tapsale and Harshal Vanjari},
        title = {Blood Group Detection Using Fingerprint Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {245-251},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169056},
        abstract = {Fingerprint pattern is the most consistent and distinguishing aspect of human identification. The fingerprint pattern cannot be modified and remains the same until the individual dies. Fingerprint verification is still considered the most crucial piece of evidence in the case of an event, even in the court of law. Each individual has a unique minutiae pattern, and the chances of resemblance are extremely low, around one in sixty-four thousand million. Even twins exhibit unique patterns. The ridge pattern is also unique, and has not changed since the birth of an individual. The method described in this work involves matching minutiae feature patterns obtained from fingerprints for a person identification system. Blood grouping has also been studied using fingerprints. Fingerprint matching was performed using ridge- frequency estimation. The spatial features were retrieved using a Gabor filter. The fingerprint scanner-based work reported here demonstrates great efficiency in image processing activities, such as image- to- binary conversion and thinning for fingerprint pattern correction and normalization.},
        keywords = {Machine Learning, Deep learning, Blood Groups, Fingerprint Map Reading, Image Processing.},
        month = {November},
        }