Biometric authentication systems offer potential advantages over traditional knowledge-based methods. However, most of the biometric systems which are extensively used lack template security and robustness. In order to address these issues, in this study, the authors have proposed a multimodal biometric system based on the combination of multiple modalities and optimal score level fusion. In addition, key features are introduced for each modality for generating cancellable biometric features. Features from individual traits are combined with corresponding key features to provide feature transformation. A robust template is generated by diffusion of individual transformed matrices using graph-based random walk cross-diffusion. In addition, individual classifier score is optimally fused using proposed multistage score level fusion model. Optimal belief masses for individual classifier are determined using cuckoo search optimisation. Wherein, optimal classifier beliefs are fused using DSmT based proportional conflict redistribution (PCR-6) rules. Experimental results demonstrate that optimal score fusion applied on cross-diffused features produce better results than existing state-of-the-art multimodal fusion schemes. On average of the outcome, equal error rate and accuracy achieved using the proposed method on four chimeric benchmarked datasets, are 2.32 and 98.316%.