Optimization of Biometric Security Systems Using Deep Learning for Enhanced Identity Verification
Sreejith Sreekandan Nair, N. Srija · 2025
Biometric security systems, driven by advanced deep learning techniques, have revolutionized identity verification processes, offering enhanced accuracy and scalability across various industries. This chapter explores the integration of deep learning in biometric systems, emphasizing its role in improving facial recognition, fingerprint, and iris scanning technologies. The key challenges in implementing deep learning, such as data privacy, computational complexity, and ethical concerns, are discussed in depth, with a focus on mitigating risks associated with biometric data protection. The chapter also highlights the future trends of deep learning in biometric systems, including the use of federated learning, privacy-preserving models, and advancements in deep neural architectures. The ethical implications surrounding user consent, system accountability, and bias in biometric recognition are examined. By providing comprehensive insights into the intersection of deep learning and biometric security, this chapter serves as a valuable resource for researchers and practitioners working towards more secure and efficient identity verification systems.