🚀 AI Project #3: Face Detection System Computer Vision Project

Welcome to your first Computer Vision project!

In this project, you'll teach a computer to identify human faces in images and live video streams.

This project introduces one of the most important AI domains:

👉 Computer Vision

Computer Vision enables machines to understand and analyze visual information from images and videos.

🎯 Project Goal

Build a Face Detection System that can:

✅ Detect faces in images

✅ Detect faces in videos

✅ Detect faces through webcam feed

✅ Draw bounding boxes around detected faces

🧠 Skills You'll Learn

Python

Functions

Loops

File Handling

Computer Vision

OpenCV

Image Processing

AI Concepts

Face Detection

Object Detection

Real-Time Video Processing

Deployment

Streamlit

📌 Difference Between Face Detection & Face Recognition

Face Detection

Answers:

Is there a face in the image?

Example: Image 3 Faces Found

Face Recognition

Answers:

Whose face is it?

Example: Face Found John

This project focuses on:

✅ Face Detection

📂 Step 1: Install Required Libraries

pip install opencv-python

Verify Installation:

import cv2
print(cv2.__version__)

🖼️ Step 2: Read an Image

import cv2
image = cv2.imread("person.jpg")
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

🔍 Step 3: Convert Image to Grayscale

Face detection works better on grayscale images.

gray = cv2.cvtColor(
    image,
    cv2.COLOR_BGR2GRAY
)

Why?

✅ Faster processing

✅ Less memory usage

✅ Better detection performance

🤖 Step 4: Load Pre-Trained Face Detector

OpenCV provides a pre-trained Haar Cascade model.

face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades +
    "haarcascade_frontalface_default.xml"
)

🎯 Step 5: Detect Faces

faces = face_cascade.detectMultiScale(
    gray,
    scaleFactor=1.1,
    minNeighbors=5
)

What Happens Here?

The model scans the image and returns coordinates:

x = 120

y = 60

width = 180

height = 180

Each coordinate represents a detected face.

🟥 Step 6: Draw Bounding Boxes

for (x,y,w,h) in faces:
    cv2.rectangle(
        image,
        (x,y),
        (x+w,y+h),
        (255,0,0),
        2
    )

Output:

😀 Face Detected

┌──────────┐

│ Face │

└──────────┘

🖥️ Step 7: Display Result

cv2.imshow(
    "Face Detection",
    image
)

cv2.waitKey(0)
cv2.destroyAllWindows()

Now detected faces appear inside rectangles.

🎥 Step 8: Real-Time Webcam Detection

This is where the project becomes exciting.

Access Webcam:

cap = cv2.VideoCapture(0)

🔄 Step 9: Process Video Frames

while True:
    success, frame = cap.read()
    gray = cv2.cvtColor(
        frame,
        cv2.COLOR_BGR2GRAY
    )
    faces = face_cascade.detectMultiScale(
        gray,
        1.1,
        5
    )
    for (x,y,w,h) in faces:
        cv2.rectangle(
            frame,
            (x,y),
            (x+w,y+h),
            (255,0,0),
            2
        )
    cv2.imshow(
        "Face Detector",
        frame
    )
    if cv2.waitKey(1) == 27:
        break

Press: ESC to stop.

🚀 Step 10: Release Camera

cap.release()
cv2.destroyAllWindows()