This paper presents two innovative utilizations of Optical Scanning Holography (OSH) to address significant challenges in secure biometric access and modulation classification systems. Current biometric authentication systems face critical vulnerabilities, such as the risk of biometric data being compromised and reused by unauthorized individuals. Additionally, existing modulation classification algorithms struggle with manual feature extraction, high computational complexity, and reduced accuracy in low Signal-to-Noise Ratio (SNR) environments. To overcome these challenges, we propose a novel OSH-based approach. In the realm of secure biometric access, we utilize OSH to generate cancellable biometric templates. This approach enhances security by ensuring that compromised biometrics can be reissued and do not expose the original data. The OSH algorithm transforms the speech signal into a 2-D matrix, which is then encrypted to create a cancellable template. This method ensures the non-invertibility and diversity of biometric data, significantly improving the security and privacy of biometric systems. For modulation classification, OSH is employed as a projection tool to create highly discriminative constellations of modulated signals. By converting modulated signals into constellation diagrams and projecting them into the OSH domain, the discrimination between different modulation types is enhanced. This method leverages the unique properties of OSH to improve classification accuracy and efficiency, addressing the limitations of traditional classification algorithms. Our evaluation metrics, including Equal Error Rate (EER) and the Area Under the Receiver Operating Characteristic Curve (AROC), demonstrate the high accuracy and robustness of our approach. The simulation results indicate that our OSH-based system achieves EER values close to zero and AROC values near one, proving its effectiveness and superiority over existing methods. Specifically, for cancellable biometric systems, the proposed method achieved an EER of 0.0035 and an AROC of 0.9991, significantly outperforming existing approaches. For modulation classification, the system maintained high classification accuracy across various SNR levels, showcasing its robustness in challenging environments. In conclusion, the proposed OSH-based approach offers a highly secure and efficient solution for both biometric authentication and modulation classification. By addressing the critical vulnerabilities in existing systems and demonstrating superior performance through rigorous evaluations, this research paves the way for more secure and reliable biometric and communication systems.
El‐Shafai et al. (Fri,) studied this question.