Real-Time Face Detection with Webcam in OpenCV using Python

Chapter 4.2: Real-Time Face Detection with Webcam

Real-time face detection is a practical application of computer vision where faces are detected live using a webcam. With OpenCV’s VideoCapture() and Haar Cascade Classifiers, you can build a simple but effective face detection system.

📷 Accessing the Webcam

Use cv2.VideoCapture(0) to access the default webcam (0 = first camera, 1 = external camera).

import cv2

# Load Haar cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

# Start webcam
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Detect faces
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30,30))
    
    # Draw rectangles around faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
    cv2.imshow("Real-Time Face Detection", frame)
    
    # Exit on 'q' key
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

⚙️ Notes

• 🔹 scaleFactor: Parameter specifying how much the image size is reduced at each scale.
• 🔹 minNeighbors: Defines how many neighbors a rectangle should have to retain it.
• 🔹 Press ‘q’ to exit the webcam window.

✅ You now know how to build a real-time face detection system using OpenCV. This is the foundation for attendance systems, security applications, and live video analytics.
👉 Next, we will learn object detection using color spaces.