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Understanding Face Detection and Recognition

Understanding Face Detection and Recognition
1. What does face detection typically return in an image?
A) Bounding box coordinates around the face
B) Image filters
C) Facial features
D) Facial landmarks
Explanation: Face detection typically returns bounding box coordinates that form a rectangle around the face.
2. What information can face analysis provide besides detecting faces?
A) Bounding boxes
B) Facial features such as nose, eyes, eyebrows, lips, and others
C) Image classification
D) Object detection
Explanation: Face analysis can provide information such as facial features like nose, eyes, eyebrows, lips, and others.
3. What is the application of training a machine learning model to identify known individuals from their facial features called?
A) Face detection
B) Face analysis
C) Facial recognition
D) Feature extraction
Explanation: Training a machine learning model to identify known individuals from their facial features is called facial recognition.
4. How is a model trained for facial recognition?
A) Using multiple images of an individual
B) Using a single image of an individual
C) Using only textual data
D) Using audio data
Explanation: A model is trained for facial recognition using multiple images of an individual.
5. What are the benefits of using facial recognition responsibly?
A) Improves system complexity
B) Improves efficiency, security, and customer experiences
C) Increases data storage requirements
D) Reduces system performance
Explanation: When used responsibly, facial recognition can improve efficiency, security, and customer experiences.
6. What service does Azure AI provide related to face detection and recognition?
A) Azure ML Service
B) Azure AI Face service
C) Azure Cognitive Search
D) Azure Data Lake
Explanation: Azure AI provides the Azure AI Face service, which includes pre-trained models to detect, recognize, and analyze faces.

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