Biometric Systems in Focus: A Review of Methods, Challenges, and Future Directions
Masroor Fatima, Ruqiya Banu, Saman Shojae Chaeikar, Maryam Khanian Najafabadi · 2024
Biometric authentication has emerged as an important component in strengthening cyber security, providing a superior alternative to conventional password-based systems because of their distinctive and difficult-to-duplicate attributes. This study examines diverse biometric techniques—including fingerprint, facial recognition, palm vein, and behavioral biometrics—and the incorporation of Artificial Intelligence (AI) methodologies such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to improve their dependability. Innovations including multi-modal biometrics, cancelable biometrics, and Genetic Encryption Algorithms (GEA) are examined, emphasizing their capacity to enhance security while mitigating privacy issues. The research additionally investigates continuous authentication inside IoT systems and future themes such as federated learning and quantum-resistant algorithms. Despite these developments, issues of computing complexity, scalability, consumer acceptance, and data privacy remain. The report concludes with recommendations for future research focused on optimizing biometric systems for broad implementation, improving data privacy, and creating adaptive, AI-driven methodologies to enhance biometric security.