Abrar M. Alajlan^1,*, Abdul Razaque²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 919-946, 2025, DOI:10.32604/cmes.2025.071996 - 30 October 2025

Abstract Biometric authentication provides a reliable, user-specific approach for identity verification, significantly enhancing access control and security against unauthorized intrusions in cybersecurity. Unimodal biometric systems that rely on either face or voice recognition encounter several challenges, including inconsistent data quality, environmental noise, and susceptibility to spoofing attacks. To address these limitations, this research introduces a robust multi-modal biometric recognition framework, namely Quantum-Enhanced Biometric Fusion Network. The proposed model strengthens security and boosts recognition accuracy through the fusion of facial and voice features. Furthermore, the model employs advanced pre-processing techniques to generate high-quality facial images and voice… More >

Ansam A. Abdulhussien^1,2,*, Mohammad F. Nasrudin¹, Saad M. Darwish³, Zaid A. Alyasseri¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 219-242, 2023, DOI:10.32604/cmc.2023.033331 - 06 February 2023

Abstract Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being… More >

Saleem Ibraheem Saleem^1,*, Adnan Mohsin Abdulazeez¹, Zeynep Orman²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2727-2754, 2021, DOI:10.32604/cmc.2021.016447 - 13 April 2021

Abstract The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In this study, we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks. This modified method is combined with a new effective rotation model to achieve an… More >