Abstract
Biometric security is a major emerging concern in the field of data security. In recent years, research initiatives in the field of biometrics have grown at an exponential rate. The multimodal biometric technique with enhanced accuracy and recognition rate for smart cities is still a challenging issue. This paper proposes an enhanced multimodal biometric technique for a smart city that is based on score-level fusion. Specifically, the proposed approach provides a solution to the existing challenges by providing a multimodal fusion technique with an optimized fuzzy genetic algorithm providing enhanced performance. Experiments with different biometric environments reveal significant improvements over existing strategies. The result analysis shows that the proposed approach provides better performance in terms of the false acceptance rate, false rejection rate, equal error rate, precision, recall, and accuracy. The proposed scheme provides a higher accuracy rate of 99.88% and a lower equal error rate of 0.18%. The vital part of this approach is the inclusion of a fuzzy strategy with soft computing techniques known as an optimized fuzzy genetic algorithm.
Highlights
Biometric security is a major emerging concern in the field of data security
The future of smart city security is based on multimodal biometrics
Supplementary traits derived from divergent modalities are used in multimodal biometric systems
Summary
Experts in the field of multimodal biometric security have developed several methods. Yang et al.[14] examined the effect of feature composition on multi-biometric device matching results using a multi-biometric framework based on fingerprints and faces that uses feature-level fusion[15]. During the fusion phase of multimodal identification technologies, Selwal et al.[21] developed a union operation of fuzzy correlations of modality templates This method accomplishes both feature fusion and the translation of several templates into a unique, reliable, noninvertible template. Pang et al.[24] suggest a novel method based on the collaborative paradigm that allows multiple city digital twins (DT) using federated learning (FL) This approach allows sharing the local strategy and provides a global model in multiple iterations at different city DT systems. Experimental results carried out at various locations show some new interesting findings in the IoT-based motion intelligence monitoring system
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