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Fusion, Multi Biometrics, Ensemble, Support Vectors, Perceptron, Probability.

Identification of person using multiple biometric is very common approach used in existing user validation of systems. Most of multibiometric system depends on fusion schemes, as much of the fusion techniques have shown promising results in literature, due to the fact of combining multiple biometric modalities with suitable fusion schemes. However, similar type of practices are found in ensemble of classifiers, which increases the classification accuracy while combining different types of classifiers. In this paper, we have evaluated comparative study of traditional fusion methods like feature level and score level fusion with the well-known ensemble methods such as bagging and boosting. Precisely, for our frame work experimentations, we have fused face and palmprint modalities and we have employed probability model - Naive Bayes (NB), neural network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning ensemble approaches namely, Boosting and Bagging are statistically well recognized. From experimental results, in biometric fusion the traditional method, score level fusion is highly recommended strategy than ensemble learning techniques.