Face and Eye Detection in OpenCV | Haar Cascade Classifier with Python

Chapter 4.1: Haar Cascade Classifiers – Face & Eye Detection

Haar Cascade Classifiers are machine learning-based approaches used for object detection. They are widely used for face and eye detection in images and videos. OpenCV provides pre-trained Haar cascades that make detection simple and fast.

📌 What is a Haar Cascade?

A Haar cascade is trained with thousands of positive and negative images. It uses features instead of pixels to detect objects quickly, making it efficient for real-time detection.

😀 Face Detection Example

OpenCV provides haarcascade_frontalface_default.xml for face detection.

import cv2

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

# Load image
img = cv2.imread("face.jpg")
gray = cv2.cvtColor(img, 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(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

cv2.imshow("Face Detection", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

👀 Eye Detection Example

Similarly, use haarcascade_eye.xml for detecting eyes within detected faces.

# Load Haar cascades
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")

for (x, y, w, h) in faces:
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]
    eyes = eye_cascade.detectMultiScale(roi_gray)
    for (ex, ey, ew, eh) in eyes:
        cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (255, 0, 0), 2)

cv2.imshow("Face and Eye Detection", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

✅ In this lesson, you learned how to use Haar Cascade Classifiers in OpenCV for face and eye detection. This method is widely used in security systems, attendance monitoring, and human-computer interaction.
👉 Next, we’ll explore real-time face detection using webcam.