SnapIdent: Face Detection and Image Capture Project SnapIdent is a simple yet effective project that demonstrates real-time face detection and image capturing using Python and OpenCV. The system uses a webcam to detect faces in real-time and captures images, which are then saved into a dedicated folder for further processing or training in face recognition systems. Project Overview SnapIdent captures and stores images of faces detected through a webcam feed. The project creates a database of face images stored in a specific directory for future use in face recognition systems or other AI applications. The images are resized for consistency, and the program automatically organizes them into a dataset folder under a user-specific subfolder. Features Real-time Face Detection: Detects faces in real-time using the webcam feed. Automatic Image Capture: Captures and saves images of detected faces. Organized Data Storage: Saves images in a structured folder under datasets/{subfolder_name}. Customizable Subfolder Name: The name of the subfolder can be customized (default: 'divi'). Confidence-Based Recognition: Recognizes a person when the confidence value is greater than or equal to 30; marks as unrecognized when the confidence value is less than or equal to 30. Simple and Lightweight: Uses OpenCV for efficient face detection and webcam interaction. Requirements Python 3.x OpenCV NumPy Install the required libraries using: pip install numpy pip install opencv-python

How to Run the Project Clone the repository: git clone https://github.com/your-username/SnapIdent.git cd SnapIdent

Ensure that the haarcascade_frontalface_default.xml file is in the same directory as the script, or update the path accordingly. python face_detection.py The program will activate your webcam and start detecting faces. It will capture images of detected faces based on confidence levels: Recognized if confidence >= 30. Unrecognized if confidence < 30. Press ESC to stop the program and close the webcam feed. Future Enhancements Data Augmentation: Incorporate techniques like rotation, flipping, and scaling to diversify the dataset and improve recognition accuracy. Face Recognition: Implement a machine learning model to identify and recognize faces from the captured dataset. Real-time Face Recognition: Enhance the project by adding real-time face recognition functionality to identify users on the fly. User Interface: Develop a simple GUI for controlling the application and viewing captured images. Database Integration: Store captured images and associated data (e.g., names, timestamps) in a database for better organization and management.