Multimodal Biometric

Multimodal biometrics aims to enhance the accuracy and robustness of biometric authentication by combining data from multiple sources, such as face, iris, fingerprint, gait, and voice. Current research emphasizes improving fusion techniques, particularly addressing challenges like missing data through imputation methods and developing adaptive fusion strategies that account for varying data quality and environmental conditions. This field is significant for advancing security applications, particularly in areas like access control and surveillance, by creating more reliable and resilient identification systems.