Face detection with deep learning in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the pre-trained face detection model.

Computer Vision

net = cv2.dnn.readNetFromCaffe('[1]', 'res10_300x300_ssd_iter_140000.caffemodel')

Drag options to blanks, or click blank then click option'

Aconfig.pbtxt

Bdeploy.prototxt.txt

Cface_detector.prototxt

Dmodel.caffemodel

Attempts:

3 left

💡 Hint

Common Mistakes

Using the weights file instead of the architecture file.

Using a TensorFlow config file instead of Caffe prototxt.

The readNetFromCaffe function requires the model architecture file, which is deploy.prototxt.txt.

2fill in blank

medium

Complete the code to prepare the input blob for the face detector.

Computer Vision

blob = cv2.dnn.blobFromImage(image, 1.0, ([1], [1]), (104.0, 177.0, 123.0))

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A300

B224

C128

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Using 224 which is common for classification but not this detector.

Using different width and height values.

The face detector expects input images resized to 300x300 pixels.

3fill in blank

hard

Fix the error in the code to extract confidence scores from detections.

Computer Vision

confidence = detections[0, 0, i, [1]]

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A2

B4

C3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 1 which is class id.

Using index 3 or 4 which are bounding box coordinates.

The confidence score is at index 2 in the detection array.

4fill in blank

hard

Fill both blanks to compute bounding box coordinates from detection.

Computer Vision

box = detections[0, 0, i, 3:7] * np.array([[1], [2], [1], [2]])

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Aw

Bh

Cimage.shape[1]

Dimage.shape[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using width for both x and y coordinates.

Using height for both x and y coordinates.

The bounding box coordinates are normalized, so multiply by image width and height respectively.

5fill in blank

hard

Fill all three blanks to draw rectangles around detected faces with confidence above threshold.

Computer Vision

if confidence > [1]:
    (startX, startY, endX, endY) = box.astype(int)
    cv2.rectangle(image, (startX, startY), (endX, endY), [2], [3])

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A0.5

B(0, 255, 0)

C2

D0.75

Attempts:

3 left

💡 Hint

Common Mistakes

Using a confidence threshold too low or too high.

Using wrong color format or thickness type.

We draw rectangles for detections with confidence above 0.5, using green color and thickness 2.

Practice

1. What is the main purpose of face detection in deep learning?

easy

A. To find and locate faces in images or videos

B. To recognize the identity of a person

C. To generate new face images

D. To remove faces from images

Solution

1. Step 1: Understand face detection

   Face detection means finding where faces are in an image, not who they belong to.

2. Step 2: Differentiate from face recognition

   Face recognition identifies who the face belongs to, which is different from detection.

3. Final Answer:

   To find and locate faces in images or videos -> Option A

4. Quick Check:

   Face detection = Find faces [OK]

Hint: Face detection locates faces, recognition identifies them [OK]

Common Mistakes:

• Confusing detection with recognition
• Thinking detection creates new images
• Assuming detection removes faces

2. Which of the following is the correct way to load a Haar Cascade face detector in OpenCV (Python)?

easy

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

Solution

1. Step 1: Recall OpenCV syntax for Haar Cascade

   OpenCV uses cv2.CascadeClassifier to load Haar Cascade XML files.

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

   Only cv2.CascadeClassifier is correct; others are invalid or do not exist.

3. Final Answer:

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

4. Quick Check:

   Use CascadeClassifier to load Haar cascades [OK]

Hint: Use CascadeClassifier to load Haar cascades in OpenCV [OK]

Common Mistakes:

• Using wrong method names like loadCascade or Cascade
• Confusing face detector with face recognizer
• Missing the .xml file extension

3. Given this code snippet, what will be the output if the image contains two faces?

import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
img = cv2.imread('group_photo.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
print(len(faces))

medium

A. 2

B. 1

C. 0

D. Error

Solution

1. Step 1: Understand detectMultiScale output

   detectMultiScale returns a list of rectangles for each detected face.

2. Step 2: Count detected faces in the image

   Since the image has two faces, len(faces) will be 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Faces detected = 2 [OK]

Hint: detectMultiScale returns all faces found; count them [OK]

Common Mistakes:

• Assuming detectMultiScale returns a boolean
• Confusing number of faces with confidence scores
• Forgetting to convert image to grayscale

4. What is wrong with this face detection code snippet?

import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
img = cv2.imread('photo.jpg')
faces = face_cascade.detectMultiScale(img)
print(len(faces))

medium

A. detectMultiScale requires a color image

B. The XML file path is incorrect

C. The image should be converted to grayscale before detection

D. print(len(faces)) should be print(faces)

Solution

1. Step 1: Check input type for detectMultiScale

   detectMultiScale expects a grayscale image, not a color image.

2. Step 2: Identify missing grayscale conversion

   The code misses converting img to grayscale before detection.

3. Final Answer:

   The image should be converted to grayscale before detection -> Option C

4. Quick Check:

   detectMultiScale needs grayscale input [OK]

Hint: Always convert images to grayscale before face detection [OK]

Common Mistakes:

• Passing color images directly to detectMultiScale
• Assuming detectMultiScale works on any image format
• Confusing print(len(faces)) with print(faces)

5. You want to improve face detection accuracy in a crowded photo using Haar Cascades. Which approach is best?

hard

A. Increase minNeighbors parameter to reduce false positives

B. Decrease scaleFactor to detect smaller faces more precisely

C. Use color images instead of grayscale for detection

D. Skip preprocessing and use raw images directly

Solution

1. Step 1: Understand scaleFactor effect

   Lower scaleFactor means the detector checks more scales, helping find smaller faces.

2. Step 2: Compare with other options

   Increasing minNeighbors reduces false positives but may miss faces; color images and skipping preprocessing reduce accuracy.

3. Final Answer:

   Decrease scaleFactor to detect smaller faces more precisely -> Option B

4. Quick Check:

   Lower scaleFactor = better small face detection [OK]

Hint: Lower scaleFactor to catch smaller faces in crowded images [OK]

Common Mistakes:

• Increasing minNeighbors too much misses faces
• Using color images instead of grayscale
• Skipping image preprocessing steps