Secure Biometric Authentication with Blockchain Integration

Biometric authentication has become a dominant approach for secure access control, with face recognition gaining widespread adoption in mobile devices, surveillance systems, and financial applications. However, traditional biometric systems are vulnerable to spoofing attacks such as photo, video, and 3D mask impersonation. This research proposes a secure face recognition framework that integrates blockchain technology for decentralized storage and verification, combined with advanced anti-spoofing techniques powered by deep learning. The use of blockchain ensures tamper-proof storage of biometric templates, while convolutional neural networks (CNNs) and liveness detection models mitigate spoofing threats. The proposed framework enhances trust, transparency, and resilience in biometric authentication systems.