Search

Cancelable biometric recognition techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research work presents an efficient cancelable biometric recognition framework that exploits an irreversible hybrid encryption algorithm. It incorporates Deoxyribonucleic, Ribonucleic Acid sequence (DNA and RNA) encryption technique, and an evolutionary optimization technique, namely Genetic Algorithms (GAs). These techniques are employed to create completely deformed templates from their original ones. Hence, the main contribution is introducing a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA technique. The proposed framework produces entirely deformed biometric templates by ciphering the main discriminative features of the biometric traits of the authorized clients. It is firstly initialized by creating several encrypted biometric images for the original users with the logistic map. After that, the initially encrypted images are transformed into vectors of a binary array. Then, they are converted to their corresponding introns, and exons, and consequently, their relevant codons are stored in the cloud database. These relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key for each template is extracted from the original biometric image through excessive permutations between pixels. The GA optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To assess the proposed framework, six different biometric databases are considered. These databases are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed encryption algorithm is compared to those of recent studies in this field, such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The simulation results prove the superior performance of the proposed framework in terms of all adopted evaluation metrics. The proposed framework provides high Area under the Receiver Operating Characteristic (AROC) curve that reaches 0.9990, low False Acceptance Rate (FAR) of 0.0015, more uniform histograms, high correlation values for genuine users, and completely hidden biometric features. In addition, from the security perspective, the proposed framework achieves good entropy, Unified Average Changing Intensity (UACI), and Number of Pixels Change Rate (NPCR) values that reach 7.9960, 33.55%, and 99.65%, respectively.

Silicon Germanium (SiGe) solar cells with different bandgaps are used in the fabrication of the crystalline-Si/SiGe (c-Si/SiGe) double-junction cell in order to enhance the performance of the c-Si single devices. The new device is simulated with SCAPS-1D by placing the c-Si junction on the top of the SiGe solar cell. Through this simulation, one can assess the performance of the c-Si/SiGe double-junction device. The upper c-Si cell achieved an efficiency of 16.66% with 40.5 $\mu \text{m}$ total thickness, while the lower cell SiGe achieved ~ 19% efficiency. By analyzing the current matching between both cells at different bandgaps for SiGe cell (1.097 eV, 1.016 eV, and 0.928 eV), a mismatch is found. But, when applying 0.882 eV bandgap for the lower cell with absorber thicknesses of 110, 100, 90, and 6.2 $\mu \text{m}$ , a matching is obtained between the two junctions. The efficiency of the designed cell is found ~ 24.7%, with a maximum current density of 30.25 mA/cm2, achieved at an open circuit voltage of 1.01 V and a total thickness of 57.2 $\mu \text{m}$ . These results indicate that the double-junction device significantly exceeds the device performance by 7.6% with equal thickness of the optimized single-junction c-Si solar cell.

In this paper, we studied the probability distribution function (PDF) of the channel gain of dynamic underwater visible light communication (UVLC) model for different types of water using goodness-of-fit (GoF). We used different water channels at different system parameters with dynamic scenarios. First, the Zemax Optics Studio simulator is used to simulate dynamic UVLC channels. UVLC links are examined using Monte Carlo Ray-Tracing (MCRT) simulation for three different water channels; namely, pure sea water channel, clear ocean water channel and coastal ocean water channel at different configuration types. With the presence of blocking divers, we added a dynamic movement in a single input multiple users (SIMU) scenario. Our simulation is based on Zemax Programming Language (ZPL) in sequence with the Zemax Optics Studio. The GoF tests are used to get the degree of fitness between the simulation data and the set of well-known candidate distributions to determine the best fit. We used the R statistical programming language and applied predefined algorithms to determine the optimum degree of fitting for each statistical result. The Kolmogorov-Smirnov (KS), Chi-Square (CS), Cramer-Von-Mises (CVM) and Anderson-Darling (AD) tests are used to represent the four GoF statistical computation techniques for each channel scenario. The received power is enhanced by 35% when the detector movement area increases from 25 m2 to 100 m2 in clear ocean water channel. The obtained results reveal that the UVLC is best represented by Weibull, Gamma or Lognormal distributions.

Recently, biometric systems are extensively and commonly utilized for authentication and verification applications. The security issue and the dependence on a specific biometric for the biometric verification process are the main challenges confronted in biometric systems. The security issue comes due to the exploitation of the original biometrics in stored servers. Therefore, if any attacks have been introduced to the stored biometrics, they will be missed indefinitely. Consequently, the stored original biometrics must be secured through maintaining and storing these templates away from exploitation in their servers. So, there is a need for designing a cancelable biometric recognition system (CBRS) that is a promising protection trend in biometric verification and authentication fields. The CBRS is based on the conversion of biometric data or its features to a different arrangement. In this article, a novel CBRS based on the suggested optical PTFT (Phase Truncated Fourier Transform) asymmetric encryption algorithm is introduced. In the proposed algorithm, two different distributions of phases in the output and Fourier planes are maintained as deciphering keys, and thus, the encryption keys will not be utilized for the decryption process. This leads to the advantage that the two ciphering keys may be utilized as public secret keys to encrypt distinct biometric images. Consequently, the suggested PTFT cryptosystem is an asymmetric encryption/decryption technique compared to the preceding related optical encryption techniques that are symmetric techniques such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The suggested PTFT asymmetric encryption algorithm also has a wonderful practical performance in security applications. One of the main contributions of the proposed optical PTFT asymmetric encryption algorithm is that it removes the linearity features of the optical OSH and DRPE symmetric encryption algorithms through its great features of the phase truncation nonlinear operation. Subsequently, this produces an encrypted biometric template with two public keys, and the authenticated user can retrieve the original biometric template utilizing two private keys with achieving a high security and cancelability performance for the stored biometrics. To confirm the efficacy of the suggested optical encryption algorithm for developing a secure CBRS, various biometric datasets of face, ear, palmprint, fingerprint, and iris images are examined and analyzed. Extensive comparative analyses are performed amongst the suggested algorithm and the optical OSH and DRPE encryption algorithms. The experimental outcomes achieved for performance quality assessment assure that the suggested CBRS is reliable, robust, and realistic. It has great security and cancelability proficiency that expose excellent cancelable biometric recognition performance even in the existence of noise. Moreover, the performed experiments declare that the suggested CBRS guarantee an average FRR (False Reject Rate) of 0.0012, EER (Equal Error Rate) of 0.0019, and FAR (False Accept Rate) of 0.0030, and an average AROC (Areas under the Receiver Operating Characteristic) of 0.9996.

Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography (rPPG) is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm. Non-skin areas have no contribution to the HR information; however, few works have tackled the issue of non-skin pixels included in the ROI. First, this paper considers improving the quality of the rPPG signal by filtering out non-skin pixels included within the ROI. The feasibility of employing skin segmentation for ROI definition is demonstrated. Then, this technique is compared with our previous real-time rPPG-based method. Moreover, we explore the effect of extracting the HR from three ROIs using signal fusion. Second, we give a comprehensive account of the examined methods in our algorithm for face detection, face tracking, skin detection, and blind signal separation. Finally, we compare our rPPG measurements with ground truth values obtained from a commercial pulse oximeter. Based on the simulation results, the proposed algorrithm significantly improves the quality of the rPPG technique.