You work in the security department of your company’s IT division. Your company has decided to try to use facial recognition to improve security on their campus. You have been asked to design a system that augments your company’s building access security by scanning the faces of people entering their buildings and recognizing the person as either an employee/contractor/consultant, who is in the company’s database, or visitor, who is not in their database.
Your company has over 750,000 employees and over 250,000 contractors and consultants across their many campus locations worldwide. These workers are all registered in their HR database. Each of these workers has an image of their face stored in the HR database. You have decided to use Amazon Rekognition for your facial recognition solution. On occasion, the Rekognition model fails to recognize visitors to the buildings. What could be the source of the problem? 

Answer options:

A.Face landmarks filters are set to a max sharpness.
B.Bounding box and confidence score for face comparison threshold tolerances are set to max values.
C.Confidence threshold tolerance is set to the default.
D.Face collection contents

Answer: D
Option A is incorrect. From the Amazon Rekognition FAQs: “Face landmarks are a set of salient points, usually located on the corners, tips or midpoints of key facial components such as the eyes, nose, and mouth. Amazon Rekognition DetectFaces API returns a set of face landmarks that can be used to crop faces, morph one face into another, overlay custom masks to create custom filters, and more.” Face landmarks don’t have a sharpness parameter.
Option B is incorrect. The bounding box and confidence score are used to determine confidence in the Rekognition comparison result. A maximum confidence score tolerance would not cause failures to recognize faces. A low confidence score tolerance would do.
Option C is incorrect. Similar to option C, the default threshold would not be a common source of recognition failure. A confidence threshold tolerance that is set too low would cause a failure in recognition.
Option D is correct. A suboptimal face collection can be the source of recognition failure. Our face collection has only one image per person. The recommendation from the Amazon Rekognition FAQs is “Besides video resolution, the quality and representative faces part of the face collections to search has a major impact. Using multiple face instances per person with variations like beard, glasses, poses (profile and frontal) will significantly improve the performance.”
Reference:
Please see the Amazon Recognition developer guide titled Detecting and Analyzing Faces.