Ant colony optimization based fuzzy binary decision tree for bimodal hand knuckle verification system

In the recent trends of touch-less biometric authentication systems, hand knuckles from dorsal part of the hand is gaining popularity as a potential candidate for verification/recognition in variety of security applications. However, most of the available knuckle verification systems offer fixed security achieved for desired level of accuracy which cannot meet the varying levels of security requirements. This paper presents a bimodal knuckle verification system which is designed to meet a wide range of applications varying from civilian to high security regions. We use ant colony optimization (ACO) to choose the optimal fusion parameters corresponding to each level of security. The developed verification system utilizes fuzzy binary decision tree (FBDT) which is aimed at decision making in two classes: genuine (accept) and imposter (reject) using matching scores computed from the knuckle database. The FBDT is implemented using fuzzy Gini index for the selection of the tree nodes. The experiments are carried out on four publicly available HongKong PolyU knuckle databases named as: left index, right index, left middle and right middle with four bimodal systems: left–right index, left–right middle, left index–middle and right index–middle. The experimental results from these four bimodal knuckle databases validate the contributions of the proposed work.

► FBDT classification is for the first time utilized for the knuckle verification.
► Fuzzified matching scores are used for learning of FBDT with two splitting measures.
► ACO based selection of fusion parameters for the first time in knuckle bimodal system.
► The proposed knuckle bimodal systems can meet varying security requirements.