Abstract: Unimodal biometric systems exhibit inherent limitations including susceptibility to noise, spoof attacks, and non-universality, which restrict their deployment in high-security domains such as crime control and law enforcement. While multimodal biometric systems mitigate these drawbacks, most existing implementations rely on shallow classifiers that degrade with large-scale data, or on deep learning models that incur significant computational overhead.