Biometric verification system based on Density and classifier based score level fusion

Selecting the most accurate rules under the fusion strategies depends on many factors such as quantity of training data, quality of given training data, understanding reliability of ground truth for training data, modeling tools etc. In our work we have adopted score level fusion scheme as it contains rich source of input data and has proximity features, we have explored promising rules such as density based likelihood features-support vector machines((LF-SVM)), likelihood features random subspace of AdaBoost (LF(RS-ADA)) and classifier based (support vector machines(SVM), random subspace of AdaBoost (RS-ADA), dempster-shafer (DS).