Perform face verification and face recognition with encodings

Face recognition problems commonly fall into one of two categories:

Face Verification "Is this the claimed person?" For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are the correct person. A mobile phone that unlocks using your face is also using face verification. This is a 1:1 matching problem.

Face Recognition "Who is this person?" For example, the video lecture showed a face recognition video of Baidu employees entering the office without needing to otherwise identify themselves. This is a 1:K matching problem.

FaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, you can then determine if two pictures are of the same person.

By the end of this assignment, you'll be able to:

Differentiate between face recognition and face verification
Implement one-shot learning to solve a face recognition problem
Apply the triplet loss function to learn a network's parameters in the context of face recognition
Explain how to pose face recognition as a binary classification problem
Map face images into 128-dimensional encodings using a pretrained model
Perform face verification and face recognition with these encodings