Face recognition concept in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does face recognition typically work?

Which step is not usually part of a face recognition system?

A Translate faces into different languages

B Detect faces in an image

C Extract features from detected faces

D Compare extracted features to known faces

💡 Hint

Think about what face recognition systems do with faces, not unrelated tasks.

Face recognition systems detect faces, extract features, and compare them. Translating faces into languages is unrelated.

❓ Model Choice

intermediate

Choosing a model for face recognition

Which model type is best suited for extracting meaningful features from face images?

A K-Means Clustering

B Recurrent Neural Network (RNN)

C Linear Regression

D Convolutional Neural Network (CNN)

💡 Hint

Consider which model type is good at processing images.

CNNs are designed to process images and extract spatial features, making them ideal for face recognition.

❓ Metrics

advanced

Evaluating face recognition accuracy

Which metric best measures how well a face recognition system correctly identifies known faces?

A BLEU Score

B Mean Squared Error

C Accuracy

D Silhouette Score

💡 Hint

Think about a metric that measures correct predictions in classification.

Accuracy measures the proportion of correct identifications, suitable for face recognition. MSE is for regression, BLEU for language, Silhouette for clustering.

🔧 Debug

advanced

Troubleshooting face recognition model training

After training a face recognition model, the training accuracy is very high but test accuracy is very low. What is the most likely cause?

A The training data has too many classes

B The model is overfitting the training data

C The test data is corrupted

D The model is underfitting the training data

💡 Hint

Think about what happens when a model learns training data too well but fails on new data.

Overfitting means the model memorizes training data but does not generalize well to new data, causing low test accuracy.

❓ Predict Output

expert

Output of face embedding extraction code

What is the output shape of the face embedding vector extracted by this code?

Computer Vision

import numpy as np
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing import image

model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
img_path = 'face.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
embedding = model.predict(x)
print(embedding.shape)

A (1, 2048)

B (224, 224, 3)

C (1, 1000)

D (7, 7, 2048)

💡 Hint

Pooling='avg' means the output is a vector, not a feature map.

ResNet50 with include_top=False and pooling='avg' outputs a 2048-length vector per image, shape (1, 2048).

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