Multi-Modal Biometric System: Technological Applications and Future Trends
Prerna Prerna, Sanjeev Indora, Dinesh Kumar Atal · 2025
Maintaining access to digital and physical resources increasingly depends on biometric authentication techniques. On the other hand, unimodal biometric systems may sometimes struggle with noise sensitivity, spoofing assaults, and intra-class differences. Multi-modal biometric systems, which combine several biometric characteristics to boost accuracy, reliability, and security, are examined in this research in terms of their evolution and use. The work offers a Hybrid Multi-Modal Biometric Authentication Model (HMMBAM) backed by blockchain technology guaranteeing template integrity and anonymity by combining sophisticated feature extraction methods and decision-level fusion. The technology solves issues in biometric variability and spoofing by means of robust data fusion and safe template management. Practical restrictions including computing complexity and sensor interoperability are examined combined with future prospects stressing lightweight algorithms, adaptive learning, and more biometric modalities. The results indicate the possibility of multi-modal biometrics to provide better authentication performance and open the door for next-generation secure systems. Multimodal biometric systems increase the accuracy and reliability of identity verification processes by combining several biometric characteristics. By merging several modalities, these systems remove limitations of unimodal biometrics include sensitivity to spoofing, environmental impacts, and individual variation. Since they may improve security in various applications, including border protection, multimodal biometric system research and development have drawn great interest. This paper investigates the challenges, answers, and advances in multimodal biometric systems to offer knowledge of future paths and solutions for robust and safe identification systems.