BLOOD GROUP DETECTION USING IMAGE PROCESSING AND FINGERPRINT

Abstract

Blood group identification plays a vital role in medical diagnosis, emergency healthcare, blood transfusion management, and forensic investigations. Conventional blood group determination methods require laboratory testing, specialized equipment, and trained personnel, which can be time-consuming and costly. Recent advancements in image processing and biometric technologies have opened new possibilities for non-invasive blood group prediction. This paper presents a blood group detection system using image processing and fingerprint analysis. The proposed framework utilizes fingerprint images as input and applies image preprocessing, feature extraction, and pattern analysis techniques to identify distinctive ridge characteristics associated with different blood groups. Image processing methods are employed to enhance fingerprint quality, remove noise, and extract relevant minutiae features such as ridge endings, bifurcations, and texture patterns. Machine learning algorithms are then used to classify fingerprints into corresponding blood group categories based on the extracted features. Experimental analysis demonstrates that the proposed approach provides an efficient, non-invasive, and automated solution for blood group prediction. The developed system has potential applications in healthcare management, biometric identification, and forensic science.