ETHNICITY CLASSIFICATION USING A DYNAMIC HORIZONTAL VOTING ENSEMBLE APPROACH BASED ON FINGERPRINT

Today, there is a fierce rivalry between ethnic groups in Nigeria on number of issues, such as the division power and resources, aversion to dominance, uneven growth. Ethnicity an identity naturally occupies prominent position political arena. It simplest most natural way for people mobilize around essential human needs security, food, shelter, economical well-being, inequity, land distribution, autonomy, recognition. Recent research has revealed potential determine individual's ethnicity based biometric data automatically. These studies reported significant advancements automatically predicting demographics facial iris traits. This success been ascribed availability sufficient amount high-quality data. There be more about likelihood that fingerprints can disclose ethnicity. A need causes this difficulty. study aims obtain fingerprint pictures via live scan among major Nigeria. For training classification images, proposed Dynamic Horizontal Voting Ensemble (DHVE) deep learning with Hybrid Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) base learner was employed. Standard performance metrics Accuracy, Recall, Precision, F1 score were used evaluate analysis model. demonstrated accuracy over 98% person's Additionally, model outperformed existing state-of-the-art models.   &nbsp