Face Recognition
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?".
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.
Steps
1- Implement the triplet loss function
2- Use a pretrained model to map face images into 128-dimensional encodings
3- Use these encodings to perform face verification and face recognition
Face verification solves an easier 1:1 matching problem; face recognition addresses a harder 1:K matching problem. The triplet loss is an effective loss function for training a neural network to learn an encoding of a face image. The same encoding can be used for verification and recognition. Measuring distances between two images' encodings allows you to determine whether they are pictures of the same person.
References:
Florian Schroff, Dmitry Kalenichenko, James Philbin (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf (2014). DeepFace: Closing the gap to human-level performance in face verification The pretrained model we use is inspired by Victor Sy Wang's implementation and was loaded using his code: https://github.com/iwantooxxoox/Keras-OpenFace. Our implementation also took a lot of inspiration from the official FaceNet github repository: https://github.com/davidsandberg/facenet