Safe access to essential systems using biometrics has gained increasing attention in the last decades. Biometrics need to be saved in a protected template away from hackers. They can be saved in a cancelable template that can be replaced in hacking scenarios. In this paper, a unimodal cancelable biometric system is introduced. Bio-signals are the biometrics signals such as voiceprint, Electroencephalography, and Electrocardiography that are tested by the proposed system. This proposed system depends on the Empirical Mode Decomposition (EMD). EMD is used to decompose the bio-signal into different intrinsic mode functions (IMFs). The first IMF carries most of the signal energy that distinguishes the bio-signal. The encryption algorithm has an essential role in cancelable template generation. Double Random Phase Encoding (DRPE) with its Random Phase Masks (RPM) is used to encrypt the first IMF after converting to 2-D format. DRPE with its random masks can be considered a non-invertible transformation. A reference signal is used to be a cover signal for the encrypted first IMF. However, the first IMF of the reference signal is replaced by the encrypted first IMF of the bio-signal to obtain the cancelable template. The verification process is performed by matching the saved first IMF and the new first IMF in their encrypted forms. Simulation analysis indicates the high performance of the proposed system. Different metrics have been investigated to evaluate the proposed system such as Equal Error Rate (EER) and Area under Receiver Operator Characteristic (AROC). This system proves its stability in the presence of different levels of white Gaussian noise where the EER is close to 0 and the AROC is close to 1.

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 × 10−8, 9.05 × 10−6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise.

The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security. To access and control sensitive environments like IoT, passwords are not recommended for high security levels. Biometrics can be used instead, but more protection is needed to store original biometrics away from invaders. This paper presents a cancelable multimodal biometric recognition system based on encryption algorithms and watermarking. Both voice-print and facial images are used as individual biometrics. Double Random Phase Encoding (DRPE) and chaotic Baker map are utilized as encryption algorithms. Verification is performed by estimating the correlation between registered and tested models in their cancelable format. Simulation results give Equal Error Rate (EER) values close to zero and Area under the Receiver Operator Characteristic Curve (AROC) equal to one, which indicates the high performance of the proposed system in addition to the difficulty to invert cancelable templates. Moreover, reusability and diversity of biometric templates is guaranteed.