Face recognition technology has emerged as a transformative solution in various sectors, offering robust mechanisms for identity verification and security applications. This paper explores the integration of FaceNet, an advanced facial recognition model, with TensorFlow to develop a comprehensive framework for secure and efficient face detection systems. By leveraging biometric authentication, the proposed framework generates unique embeddings for each individual, facilitating precise identity verification. Additionally, we implement data de-duplication techniques within the system to minimize storage redundancy and enhance the scalability of cloud-based applications. While the integration of a secure database is pivotal to safeguarding sensitive information, this aspect is briefly overviewed in the abstract, with detailed discussions reserved for subsequent sections. The study further highlights the system's application potential in real-world scenarios, emphasizing its contribution to the broader domain of intelligent systems for sustainable industrial infrastructure.