Deep learning Project : Face recognition and verification
Face Verification with Anchor images embedded
Face recognition problems commonly fall into 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 (https://www.youtube.com/watch?v=wr4rx0Spihs) 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.
In this notebook, you will:
- Implement the triplet loss function
- Use a pre-trained model to map face images into 128-dimensional encodings
- Use these encodings to perform face verification and face recognition