The given solutions in this project are only for reference purpose.
Welcome! In this project, we're going to build a face recognition system. Many of the ideas presented here are from FaceNet.
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, we can then determine if two pictures are of the same person.
By the end of this project, we'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
https://www.coursera.org/learn/convolutional-neural-networks
https://www.deeplearning.ai/program/deep-learning-specialization/