Cancelable biometric authentication leveraging empirical mode decomposition and quaternion representations for IoT security.

Biometric authentication is essential for securing Internet of Things (IoT) devices, yet the vulnerability of biometric data to breaches underscores the necessity for improved security measures. Cancelable biometric sysems, which convert original biometric data into non-reversible templates, offer a strong solution for user privacy preservation. This paper presents an innovative method for creating cancelable biometric templates by integrating Empirical Mode Decomposition (EMD) and quaternion mathematics. The EMD disaggregates biometric data into Intrinsic Mode Functions (IMFs), whereas quaternion transformations guarantee the safety and non-reproducibility characteristics of the templates. The proposed method ensures template diversity and IoT systems' efficiency. Experimental assessments indicate the method robustness against possible attacks, attaining an Area under the Receiver Operating Characteristic curve (AROC) of 0.9997 and an almost negligible Equal Error Rate (EER). Moreover, the system has a minimal computational cost, rendering it appropriate for resource-limited IoT settings. These findings highlight the method capacity to tackle significant security issues, while ensuring optimal performance in practical applications.