Multimodal biometric authentication system leveraging optimally trained ensemble classifier using feature-level fusion.

ObjectiveThis study aims to enhance cybersecurity by implementing a robust biometric-based authentication approach. A Multimodal Biometric System (MBS) is proposed, utilizing feature-level fusion of human facial (physiological) and speech (behavioral) features to improve security, accuracy, and user convenience. The system addresses the limitations of traditional authentication methods, including unimodal biometrics and password-based security.BackgroundIn the modern digital landscape, human-computer interaction and digital platforms play a crucial role in daily life. With billions of users engaging in social media, financial transactions, and e-commerce, the demand for secure authentication mechanisms has intensified. However, the increasing sophistication of cyber threats poses significant risks, undermining trust, security, and confidence in digital systems. The proposed MBS incorporates improved proposed techniques for feature extraction, feature level fusion strategies and an ensemble classification model combining Bi-LSTM and DCNN. To optimize performance, the system is enhanced using an improved bio-inspired Manta Ray Foraging Optimization (MRFO) algorithm.ResultsThe system's performance was evaluated using two publicly available Voxceleb1 and VidTIMIT datasets, achieving accuracy rates of 98.23% and 97.92%, with Equal Error Rates (EERs) of 3.23% and 3.62%, respectively.ConclusionThe proposed approach outperforms conventional optimization techniques and existing state-of-the-art MBS. As a contactless and non-intrusive authentication system, it enables seamless data acquisition through devices equipped with cameras and microphones, such as smartphones, ensuring real-time processing of biometric modalities. This contactless MBS presents a viable solution for secure and hygienic authentication in applications requiring high cyber resilience, including banking, e-commerce and other digital security domains. This research enhances cybersecurity by proposing a Multimodal Biometric System (MBS) that integrates feature-level fusion of facial (physiological) and speech (behavioral) traits. The approach improves security, accuracy, and user convenience while addressing hygiene concerns. It overcomes the limitations of traditional authentication methods, including unimodal biometrics and password-based security vulnerabilities.