An accurate hand-based multimodal biometric recognition system with optimised sum rule for higher security applications

Abstract

This paper presents a multimodal biometric recognition system using palm print, finger geometry and dorsal palm vein modalities. A specific image acquisition system is designed, fabricated and database of 150 users is created. DWT technique for features extraction is used for palm print and dorsal palm vein modalities. Performance analysis for individual modality is done using receiver operating characteristics and accuracies of 98.775%, 98.45% and 97.60% are obtained respectively for PP, FG and DPV modalities. Further the multimodal system is proposed along with a novel basis for optimally choosing the weights. The score level fusion is done using these optimised weights. Testing, validation and benchmarking of the algorithms are done using our own database, as well as the standard database available on the net. The proposed multimodal system gives enhanced accuracy of 99.80% with very low FAR level of 0.0001.