Published Paper Details:

This project aims to develop a system for face recognition and data management using machine learning and web integration. The system will utilize deep learning algorithms for face detection and recognition, and a database for storing and managing user information. The web integration will enable remote access to the system, allowing users to view their profile and make updates as needed. The system will have potential applications in security, access control, and personalized user experiences and it has become an important tool for many applications, including security, law enforcement, and personal identification. However, managing large amounts of face data can be challenging, especially when it comes to ensuring accuracy and privacy. Machine learning algorithms can help automate the process of recognizing and managing face data, while web integration can provide a convenient and secure way to access and share this data. These advancements have made it possible to recognize faces with high accuracy, even in challenging conditions such as low light, occlusion, and pose variations. However, managing large amounts of face data can be challenging, especially when it comes to ensuring accuracy and privacy. There is a need for a systematic approach to collecting, processing, and managing face data. Machine learning algorithms can help automate the process of recognizing and managing face data. algorithms can be used for tasks such as face detection, face recognition, face tracking, and face attribute analysis. Machine learning algorithms can also be used for data management tasks such as face clustering, face labeling, and face attribute annotation. Web integration can provide a convenient and secure way to access and share face data. A web-based interface can allow authorized users to search, browse, and download face data. Web-based services can also provide a platform for sharing face data with other organizations, such as law enforcement agencies or research institutions. However, there are still many challenges to be addressed, such as privacy concerns, data bias, and ethical issues. Further research is needed to develop robust and trustworthy face recognition systems that respect privacy and human rights.