Multimodal biometrics use more than one means of biometric identification to achieve higher detection accuracy, since sometimes a single biometric is not good enough used to do identification and codification. Sclera and finger print strain is a new counterpart vein recognition method using a two-stage parallel approach for documentation and bouting. First, modeled a rotation-and rule-invariant Y shape descriptor based feature extraction method to efficiently eliminate most unlikely matches. Second, developed a weighted polar line sclera descriptor structure to incorporate mask information to reduce Graphics processing unit (GPU) awareness cost. Third, modeled a coarse-tofine two-rung matching approach. Finally, developed a weighing scheme to map the subtasks to GPU processing things. The experimental upshots show that the urged method can achieve dramatic processing speed improvement without compromising the detection accuracy. Here examined the previously proposed finger-strain identification approaches and develop a new approach that illustrates it superiority over prior published efforts. Then is to progress and examine two new score-level blends, i.e., holistic and non linear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed scheme. The aim is to combine the upshots obtained by different biometric traits and significantly improve the overall accuracy of the biometric system.