Especially in the financial sector and law enforcement, biometric authentication technologies are becoming crucial. Unimodal systems were utilised in the past for biometrics, but multimodal biometrics is now in vogue. Multimodal biometrics is a crucial part of pattern recognition and has received a lot of attention in the scientific community. Although multimodality is frequently manipulated by unimodal systems, they frequently accomplish numerous tasks. In order to obtain high classification accuracy and low mistake rate, this research looks into the safe authentication procedure of a multimodal system employing fingerprint and facial recognition. However, the developed optimisation method's method for carrying out the fusion process involves the chance selection of two logical operators. The exponentially weighted moving average (EWMA) and WWO (water wave optimisation) are combined in order to create the scale-invariant feature transformation based fuzzy set exponential water wave optimisation (FEWWO). SIFT and FEWWO of face category, fingerprint ridge, and feature extraction are used to optimise and extract features. On the basis of the FVC200 fingerprint and Ollivati face datasets, the suggested solution is assessed. By obtaining low false positive and rejection rates, the suggested method can improve the realism of multimodal systems and reach our desired target of accuracy of about 99.2%.