Deep rule-based classifier for finger knuckle pattern recognition system

In this paper, we proposed a novel finger knuckle pattern (FKP) based personal authentication system using multilayer deep rule based (DRB) classifier. The presented approach is completely data-driven and fully automatic. However, the DRB classifier is generic and can be used in variety of classification or prediction problems. In particular, from the input finger knuckle, two kinds of features (i.e., Binarized Statistical Image Features and Gabor Filer bank) are extracted, which are then fed to fuzzy rules based DRB classifier to determine whether the user is genuine or impostor. Experimental results in the form of accuracy, error equal rate (EER) and receiver operating characteristic (ROC) curves demonstrate that presented DRB classifier is a powerful tool in FKP based biometric identification system. Experiments are reported using publicly available FKP PolyU database provided by University of Hong Kong. Experiments using this database show that the presented framework, in this study, can attain performance better than previously proposed methods. Moreover, score level fusion of all FKP modalities with BSIF + DRB yielded an equal error rate of 0.19% and an accuracy of 99.65%.