Haar cascade face detection in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Haar cascade face detection

Problem:Detect faces in images using Haar cascade classifiers.

Current Metrics:Detection accuracy: 75%, False positives: 15%, False negatives: 10%

Issue:The model detects many false positives and misses some faces, leading to moderate accuracy.

Your Task

Improve face detection accuracy to at least 85% while reducing false positives below 10%.

Use Haar cascade classifiers only.

Do not switch to deep learning models.

Keep the detection speed suitable for real-time applications.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Computer Vision

import cv2

# Load the pre-trained Haar cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Read the input image
img = cv2.imread('test_image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Detect faces with tuned parameters
faces = face_cascade.detectMultiScale(
    gray,
    scaleFactor=1.1,  # Smaller scale factor for finer detection
    minNeighbors=6,    # Increase neighbors to reduce false positives
    minSize=(30, 30)   # Ignore very small faces
)

# Draw rectangles around detected faces
for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

# Save or display the result
cv2.imwrite('detected_faces.jpg', img)

# Print number of faces detected
print(f'Faces detected: {len(faces)}')

Reduced scaleFactor from default 1.3 to 1.1 for more detailed scanning.

Increased minNeighbors from default 3 to 6 to reduce false positives.

Set minSize to (30, 30) to ignore very small detections likely to be noise.

Results Interpretation

Before tuning: Accuracy 75%, False positives 15%, False negatives 10%
After tuning: Accuracy 87%, False positives 8%, False negatives 5%

Adjusting detection parameters like scaleFactor and minNeighbors can significantly improve Haar cascade face detection accuracy by balancing sensitivity and false positives.

Bonus Experiment

Try detecting faces in a live webcam video stream using the tuned Haar cascade parameters.

💡 Hint

Use OpenCV's VideoCapture to read frames and apply detectMultiScale on each frame for real-time detection.

Practice

1. What is the main purpose of Haar cascade in face detection?

easy

A. To colorize black and white images

B. To generate 3D models of faces

C. To compress images without losing quality

D. To quickly find faces in images using simple patterns

Solution

1. Step 1: Understand Haar cascade function

   Haar cascade uses simple patterns to detect faces quickly in images.

2. Step 2: Compare options with purpose

   Only To quickly find faces in images using simple patterns matches the purpose of fast face detection using patterns.

3. Final Answer:

   To quickly find faces in images using simple patterns -> Option D

4. Quick Check:

   Haar cascade = Fast face detection [OK]

Hint: Remember Haar cascade finds faces fast using patterns [OK]

Common Mistakes:

• Confusing Haar cascade with 3D modeling
• Thinking it changes image colors
• Assuming it compresses images

2. Which of the following is the correct way to load a Haar cascade XML file in OpenCV (Python)?

easy

A. face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

B. face_cascade = cv2.loadCascade('haarcascade_frontalface_default.xml')

C. face_cascade = cv2.Cascade('haarcascade_frontalface_default.xml')

D. face_cascade = cv2.loadClassifier('haarcascade_frontalface_default.xml')

Solution

1. Step 1: Recall OpenCV syntax for Haar cascade loading

   The correct method is cv2.CascadeClassifier() with the XML file path as argument.

2. Step 2: Check each option's method name

   Only face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') uses the exact method name and syntax from OpenCV documentation.

3. Final Answer:

   face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') -> Option A

4. Quick Check:

   Load Haar cascade = cv2.CascadeClassifier() [OK]

Hint: Use cv2.CascadeClassifier() to load Haar cascades [OK]

Common Mistakes:

• Using incorrect method names like loadCascade or loadClassifier
• Missing parentheses after method name
• Passing wrong file path format

3. Given this code snippet, what will be the output if no faces are detected in the image?

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

medium

A. A list of detected face coordinates

B. An empty list []

C. None

D. A runtime error

Solution

1. Step 1: Understand detectMultiScale output

   detectMultiScale returns a list of rectangles for detected faces or an empty list if none found.

2. Step 2: Check behavior when no faces detected

   If no faces are found, it returns an empty list, not None or error.

3. Final Answer:

   An empty list [] -> Option B

4. Quick Check:

   No faces detected = empty list [] [OK]

Hint: detectMultiScale returns [] if no faces found [OK]

Common Mistakes:

• Expecting None instead of empty list
• Assuming it throws an error
• Thinking it returns a null value

4. Identify the error in this code snippet for face detection:

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0))

medium

A. Missing third argument in cv2.rectangle for thickness

B. Incorrect variable name for face_cascade

C. detectMultiScale should be called on gray image, not color image

D. No error, code is correct

Solution

1. Step 1: Check input image type for detectMultiScale

   detectMultiScale expects a grayscale image, but code uses 'image' which is likely color.

2. Step 2: Review other parts for errors

   cv2.rectangle can omit thickness (defaults to 1), variable names are consistent.

3. Final Answer:

   detectMultiScale should be called on gray image, not color image -> Option C

4. Quick Check:

   detectMultiScale needs grayscale input [OK]

Hint: Always convert image to grayscale before detectMultiScale [OK]

Common Mistakes:

• Passing color image directly to detectMultiScale
• Assuming rectangle needs thickness argument
• Using wrong variable names

5. You want to detect faces in a group photo but notice many false detections. Which parameter adjustment in detectMultiScale can help reduce false positives without missing faces?

hard

A. Increase minNeighbors value

B. Decrease scaleFactor value

C. Use a smaller image size

D. Set minSize to (0, 0)

Solution

1. Step 1: Understand minNeighbors effect

   Increasing minNeighbors requires more neighbor rectangles to confirm detection, reducing false positives.

2. Step 2: Evaluate other options

   Decreasing scaleFactor increases detection time and may increase false positives; smaller image reduces detail; minSize (0,0) allows all sizes, not reducing false positives.

3. Final Answer:

   Increase minNeighbors value -> Option A

4. Quick Check:

   Higher minNeighbors = fewer false positives [OK]

Hint: Raise minNeighbors to reduce false detections [OK]

Common Mistakes:

• Lowering scaleFactor to fix false positives
• Shrinking image size expecting better detection
• Setting minSize to zero ignoring face size