Fuzzy binary decision tree for biometric based personal authentication

The use of fuzzy decision trees is yet to be ascertained for the biometric based personal authentications. This paper therefore presents a fuzzy binary decision tree (FBDT) algorithm for decision making on two classes: genuine and imposter using matching scores computed from the biometric databases. The proposed FBDT makes use of two criteria: fuzzy Gini index and fuzzy entropy for the selection of the tree nodes. The fuzzy membership functions can be automatically computed from the training scores and these are employed in two modes: Same function mode, where only one membership function is used for both the classes and Different function mode, where separate functions are used for both the classes. The parameters computed at the learning stages are used for the classification of the claimed identity in any of the two classes. Over-fitting of feature data often results in false branches in the decision trees. So the pruning of the tree is required with the consequent increase in computational complexity. Most of the FBDTs in this work are found to have lesser size than DTs as ascertained from the experimental results. The proposed FBDT is tested on two publically available databases and it fares well over its crisp counterpart.