Face recognition concept in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image for face recognition.

Computer Vision

import cv2
image = cv2.imread([1])
print(image.shape)

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

Aload('face.jpg')

B'face.jpg'

Cface.jpg

Dread('face.jpg')

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name

Using a function that does not exist like load() or read()

The cv2.imread function requires the file path as a string, so it must be in quotes.

2fill in blank

medium

Complete the code to convert the image to grayscale for face detection.

Computer Vision

gray_image = cv2.cvtColor(image, [1])
print(gray_image.shape)

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

Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_BGR2RGB

Ccv2.COLOR_RGB2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using RGB instead of BGR conversion codes

Converting grayscale back to color by mistake

To convert a color image to grayscale, use cv2.COLOR_BGR2GRAY.

3fill in blank

hard

Fix the error in the face detection code by filling the correct method name.

Computer Vision

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
facedetect = face_cascade.[1](gray_image, scaleFactor=1.1, minNeighbors=5)
print(len(facedetect))

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

AdetectMultiScale

BdetectFaces

CfindFaces

Ddetect_face

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect method names that do not exist

Confusing method names with other libraries

The correct method to detect faces using Haar cascades in OpenCV is detectMultiScale.

4fill in blank

hard

Fill both blanks to create a dictionary of face locations with width and height.

Computer Vision

faces_info = {i: (x, y, [1], [2]) for i, (x, y, w, h) in enumerate(facedetect)}
print(faces_info)

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Aw

Bh

Cx

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Using x or y instead of width and height

Mixing up the order of values

The width and height of each detected face are w and h respectively.

5fill in blank

hard

Fill all three blanks to draw rectangles around detected faces on the image.

Computer Vision

for (x, y, w, h) in facedetect:
    cv2.rectangle(image, ([1], [2]), (x + w, y + h), [3], 2)
cv2.imshow('Faces', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

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

Ax

By

C(255, 0, 0)

D(0, 255, 0)

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping x and y coordinates

Using wrong color tuples

The rectangle starts at (x, y) and uses blue color (255, 0, 0) in BGR format.

Practice

1. What is the main goal of face recognition in computer vision?

easy

A. To convert images into black and white

B. To detect objects like cars and trees in images

C. To generate new faces from random noise

D. To identify or verify a person using their facial features

Solution

1. Step 1: Understand the purpose of face recognition

   Face recognition is designed to find out who a person is by looking at their face.

2. Step 2: Compare with other options

   Options A, B, and C describe different tasks unrelated to identifying people by faces.

3. Final Answer:

   To identify or verify a person using their facial features -> Option D

4. Quick Check:

   Face recognition = Identify person by face [OK]

Hint: Face recognition means finding who the face belongs to [OK]

Common Mistakes:

• Confusing face recognition with object detection
• Thinking face recognition creates new images
• Mixing up image color processing with recognition

2. Which of the following is the correct step to train a face recognition model?

easy

A. Collect face images with labels and train the model to learn features

B. Randomly assign labels to images without training

C. Use face images only without any labels

D. Train the model using only text data about faces

Solution

1. Step 1: Identify training requirements

   Training a face recognition model needs images and their correct labels to learn who is who.

2. Step 2: Eliminate incorrect options

   Options A, B, and D miss key parts: no labels (A), random labels (B), or no images (D), which won't train properly.

3. Final Answer:

   Collect face images with labels and train the model to learn features -> Option A

4. Quick Check:

   Training needs images + labels [OK]

Hint: Training needs labeled face images, not random or unlabeled data [OK]

Common Mistakes:

• Ignoring the need for labels during training
• Thinking unlabeled images are enough
• Confusing text data with image data for training

3. Given this Python snippet for face recognition, what will be the output?

known_faces = {"Alice": [0.1, 0.2, 0.3], "Bob": [0.4, 0.5, 0.6]}
new_face = [0.1, 0.2, 0.31]

# Simple distance check
def recognize(face):
    for name, features in known_faces.items():
        dist = sum((f - nf) ** 2 for f, nf in zip(features, face)) ** 0.5
        if dist < 0.05:
            return name
    return "Unknown"

print(recognize(new_face))

medium

A. Bob

B. Alice

C. Unknown

D. Error

Solution

1. Step 1: Calculate distance between new_face and known faces

   Distance to Alice: sqrt((0.1-0.1)^2 + (0.2-0.2)^2 + (0.3-0.31)^2) = sqrt(0 + 0 + 0.0001) = 0.01 < 0.05

2. Step 2: Check distance condition and return name

   Since distance to Alice is less than 0.05, function returns "Alice" immediately.

3. Final Answer:

   Alice -> Option B

4. Quick Check:

   Distance < 0.05 means match = Alice [OK]

Hint: Check distance to known faces; closest under threshold wins [OK]

Common Mistakes:

• Calculating distance incorrectly
• Confusing which face is closer
• Ignoring the threshold condition

4. The following code is meant to recognize a face but always returns "Unknown". What is the bug?

known_faces = {"Alice": [0.1, 0.2, 0.3]}
new_face = [0.1, 0.2, 0.3]

def recognize(face):
    for name, features in known_faces.items():
        dist = sum((f - nf) ** 2 for f, nf in zip(features, face)) ** 0.5
        if dist > 0.05:
            return name
    return "Unknown"

print(recognize(new_face))

medium

A. The new_face list has wrong length

B. The known_faces dictionary is empty

C. The distance condition should be less than 0.05, not greater

D. The function does not return anything

Solution

1. Step 1: Analyze the distance condition in the if statement

   The code returns name if distance > 0.05, but it should return if distance < 0.05 to indicate a match.

2. Step 2: Understand why it always returns "Unknown"

   Since distance is 0 (exact match), condition dist > 0.05 is false, so loop never returns name.

3. Final Answer:

   The distance condition should be less than 0.05, not greater -> Option C

4. Quick Check:

   Match if distance < threshold, not > [OK]

Hint: Match when distance is small, not large [OK]

Common Mistakes:

• Using wrong comparison operator in distance check
• Assuming empty dictionary causes issue
• Ignoring return statements in loop

5. You want to improve a face recognition system to handle many people with similar features. Which approach helps most?

hard

A. Use a deeper neural network to extract more detailed face features

B. Reduce the number of training images per person

C. Ignore feature differences and use only face color

D. Train the model without labels to save time

Solution

1. Step 1: Understand the challenge of similar faces

   When faces look alike, the model needs better features to tell them apart.

2. Step 2: Evaluate options for improving feature extraction

   A deeper neural network can learn more complex and detailed features, improving recognition accuracy.

3. Final Answer:

   Use a deeper neural network to extract more detailed face features -> Option A

4. Quick Check:

   Better features = deeper network [OK]

Hint: More complex models capture finer face details [OK]

Common Mistakes:

• Reducing training data lowers accuracy
• Ignoring features and using color is ineffective
• Training without labels prevents learning identities