Robust Multimodal Biometric Recognition Based on Joint Sparse Representation

Abstract

In this paper, we concurrently consider the correlations and the information of coupling among the modalities of biometric. A computation of multimodal quality is also suggested for weighing every procedure as bonded. Moreover, we generalize the algorithm for handling the data by non-linearity. Also we have task i.e., optimizing is resolved by utilizing a method of proficient alternative direction. Several researches explain that the suggested method will compare favorably along with competing fusion-based schemes. The customary methods of the biometric recognition depend on a solitary biometric sign for confirmation. Although the benefit of utilizing the numerous resources of data to establish the uniqueness which has been broadly identified, the models that are computational for the multimodal biometric identification have only the attention of obtained recently. We recommend a representation of multimodal sparse technique, which will represent the figures of test by a scattered linear mixture of training records, whilst restraining the studies from dissimilar test subject’s modalities to allocate the sparse illustrations.