model for face detection

Face Detection:

The model is trained to identify and locate human faces in images, regardless of varying lighting conditions, face orientations, and expressions.

Rectangle Drawing:

Once a face is detected, the model draws a rectangle around the face, visually marking its location within the image.

The rectangles are accurately positioned and scaled to fit the detected faces.

Real-time Processing:

The model is capable of processing images in real-time, making it suitable for applications like live video feeds or real-time photo analysis.

Tools and Techniques:

Computer Vision Libraries:

OpenCV: Used for image processing, including face detection and drawing rectangles around the detected faces.

Haar Cascades or Deep Learning-based Models: Utilized for the actual face detection task.

Programming Language:

Python: The primary language used for implementing the model, leveraging its rich ecosystem of libraries for computer vision.