Learn which machine learning system uses bounding box coordinates to form rectangles around a person’s features. Understand the role of face detection in AI systems.
Table of Contents
Question
Which of the following machine learning systems returns bounding box coordinates that form a rectangle around a person’s features?
A. Head-pose estimation
B. Image segmentation
C. Face detection
D. Optical character recognition (OCR)
Answer
C. Face detection
Explanation
Bounding boxes are rectangular outlines used in computer vision to localize and identify objects or features within an image. They are defined by coordinates, typically the top-left and bottom-right corners, forming a rectangle around the object of interest. Among the options provided, face detection is the system that specifically returns bounding box coordinates to enclose a person’s facial features.
Here’s why face detection is the correct answer:
Bounding Boxes in Face Detection
Face detection algorithms, such as those used in frameworks like OpenCV or Amazon Rekognition, generate bounding boxes around detected faces. These boxes localize the face within an image by providing coordinates for its boundaries (e.g., top-left corner, width, and height).
This process is fundamental for applications like facial recognition, emotion analysis, and pose estimation.
Comparison with Other Options
A. Head-pose estimation: While head-pose estimation analyzes the orientation of a person’s head (e.g., tilt or rotation), it does not inherently involve generating bounding boxes around facial features.
B. Image segmentation: Image segmentation assigns a label to every pixel in an image to delineate objects or regions precisely. It provides more granular detail than bounding boxes and does not use rectangular coordinates.
D. Optical character recognition (OCR): OCR focuses on detecting and recognizing text within images, often using bounding boxes for text regions but not for a person’s features.
In summary, face detection is the machine learning system that utilizes bounding boxes to enclose and identify facial features accurately. This capability is crucial for tasks requiring spatial localization of faces in images or videos.
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