Efficient implementation of double random phase encoding and empirical mode decomposition for cancelable biometrics

Biometric-based systems for secure access to different services have gained a significant attention in recent years. To ensure the protection of biometric data from potential hackers, it is crucial to store them in the form of secure templates. Cancelable templates offer an effective solution through allowing template replacement in case of security breaches. In this paper, we propose a novel unimodal cancelable biometric system that works on bio-signals such as voiceprint, electroencephalography (EEG), and electrocardiography (ECG) signals. The key feature of our proposed system is the utilization of Empirical Mode Decomposition (EMD) to decompose the bio-signals into different Intrinsic Mode Functions (IMFs). Among these IMFs, the first IMF, which carries the majority of the signal energy and distinguishes the bio-signal, plays a pivotal role in our system. To ensure the security of the cancelable biometric template, an encryption algorithm is employed. We use the Double Random Phase Encoding (DRPE) algorithm along with its random phase masks to encrypt the first IMF after converting it into 2D format. The use of DRPE and its random masks ensures a non-invertible transformation, which enhances the security of the encrypted data. To generate the cancelable template, we replace the first IMF of a reference signal with the encrypted first IMF obtained from the bio-signal. The resulting template retains the essential distinguishing characteristics of the bio-signal, while safeguarding its security. The verification process in our system involves matching of the encrypted first IMF of the stored templates with the encrypted first IMF of a new input signal. Extensive simulation analysis has been conducted to evaluate the performance of the proposed system. Various metrics, including Equal Error Rate (EER) and Area under Receiver Operating Characteristic curve (AROC), have been considered. The results demonstrate the high performance and stability of our system, even in the presence of different levels of white Gaussian noise, with an EER close to 0 and an AROC close to 1. In conclusion, our work presents an efficient implementation of the DRPE and EMD for the development of a robust and secure cancelable biometric system. The proposed system shows promising results and holds great potential for enhancing the security and reliability of biometric-based access control.