A Hybrid Iris-Voice Biometric Framework for Robust Identification in the Presence of Ocular Impairments

Accurate and reliable biometric identification is essential in high-security applications such as healthcare, finance, and airport control. However, traditional unimodal systems—relying solely on iris or voice recognition—are prone to environmental and physiological limitations, including issues like ocular injuries and acoustic interference. To overcome these challenges, this paper proposes a hybrid biometric framework that seamlessly integrates iris and voice modalities using an dynamic fallback strategy. The system prioritizes iris recognition, employing a robust segmentation approach based on the Expectation-Maximization (EM) algorithm, which models iris texture through Gamma and Normal mixture distributions. When iris data is incomplete or compromised, the system adaptively transitions to a BiLSTM–CNN based voice recognition module. This dual-layer architecture leverages feature-level fusion to enhance decision accuracy across both modalities. Experimental validation using the CASIA and VoxCeleb datasets demonstrates that the proposed hybrid system achieves an identification accuracy of 96.4%, substantially outperforming unimodal counterparts. Further statistical analysis—including 95% confidence intervals and paired t-tests (p-value = 0.0002)—confirms the robustness and reliability of the approach. Overall, this solution presents a scalable and resilient method for biometric authentication in complex, multimodal environments.