Project Title : Real-Time Face Recognition

Objective:
To build a real-time system that can detect and recognize human faces using a camera feed, powered by artificial intelligence and computer vision.

What It Does:
It identifies people by comparing live camera input to stored face data using AI models.

Key Concepts:

Face Detection: Locating faces in images or video.

Face Recognition: Identifying whose face it is.

Uses feature extraction and comparison using machine learning or deep learning.

Steps Involved:

Face Dataset Collection:

Capture or collect images of known individuals.

Label each image with the person’s name or ID.

Preprocessing:

Resize and normalize images.

Convert images to grayscale (optional).

Extract facial features (using embeddings).

Model Building:

Use pre-trained models like FaceNet, VGG-Face, or Dlib for facial feature extraction.

Store facial embeddings in a database.

Compare real-time camera input with stored embeddings using cosine similarity or Euclidean distance.

Real-Time Implementation:

Use webcam to capture video frames.

Detect faces using Haar cascades, Dlib, or OpenCV DNN.

Recognize the face by comparing with the known database.

Evaluation:

Test with known and unknown faces.

Measure accuracy, false positives, and recognition speed.

Deployment (Optional):

Build a desktop or web app with GUI.

Use in attendance systems or access control.

Applications:

Smart attendance systems

Security and surveillance

Unlocking devices with your face

Personalized user experience

Tools & Technologies:

Languages: Python

Libraries: OpenCV, Dlib, Face Recognition, NumPy, TensorFlow/Keras

Hardware: Webcam or mobile camera