Enhancing security and accuracy in biometric systems through the fusion of fingerprint and gait recognition technologies

In the evolving landscape of security technology, biometric systems are pivotal for unique identification through physiological or behavioural traits. This research focuses on enhancing biometric system security and accuracy by integrating fingerprint and gait recognition technologies. Fingerprint recognition is valued for its precision and ease of data acquisition, while gait recognition offers non-invasiveness and resistance to obfuscation. The study explores feature and score level fusions of these modalities, utilising advanced algorithms to optimise the integration and elevate recognition performance. Experimental evaluations demonstrate that the proposed multimodal system not only outperforms unimodal systems but also strengthens robustness against spoofing attacks. Key contributions include a novel gait feature extraction technique compatible with fingerprint features and an optimised score-level fusion algorithm, significantly enhancing accuracy and security. Biometric security systems have become integral to modern security architectures, leveraging unique physiological and behavioural characteristics to authenticate individuals.