Face Detection

Face Detection System is a sophisticated face recognition solution designed for use in various industries, such as hotels and casinos, for customer identification and security purposes. The system allows for the registration of individuals with a single photograph, enabling real-time identification through surveillance screens. If an individual is not registered, they are identified with an incremented numerical tag (e.g., 0, 1, ... up to infinity). This system can be utilized to recognize and manage individuals on a blacklist, ensuring enhanced security for the business.

Programming Language: Python
Framework: OpenVINO
Tools: OpenCV for image processing, TensorFlow for machine learning
Platforms: PC (Windows, Linux)

*Real-time Face Recognition: Efficiently identifies registered individuals through live video feeds.
*Incremental Tagging: Assigns numerical tags to unregistered individuals for easy tracking.
*Blacklist Identification: Detects and alerts about blacklisted individuals to enhance security.
*Machine Learning Integration: Utilizes machine learning models for accurate face recognition and identification.
*Database Management: Manages individual records and recognition data through a robust database system.

The development of the Face Detection System was a collaborative effort between myself and another developer. My responsibilities included developing the face recognition model using machine learning techniques and managing the database interactions for storing and retrieving individual records. We encountered challenges in optimizing the recognition accuracy and ensuring the system's performance in real-time conditions. Through iterative testing and optimization, we successfully created a reliable and efficient face detection system suitable for various business applications.