Robust analysis of multibiometric fusion versus ensemble learning schemes: A case study using face and palmprint

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, asmuch of the fusion techniques have shownpromising results in literature, due to the fact of combining multiple biometric modalities with suitable fusion schemes. However, similar typeofpracticesarefound in ensemble of classifiers, which increasesthe classification accuracy while combining different types of classifiers. In this paper,we have evaluated comparative study of traditional fusion methods like featureleveland score level fusion withthewell-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) classifiersfor our experimentation. Nevertheless,machine learning ensemble approaches namely,Boosting and Baggingare statistically well recognized. From experimental results, in biometric fusion the traditional method,score level fusion is highly recommended strategythan ensemble learning technique