One-Shot Learning for Face Recognition
Last Updated on June 11, 2020
One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future.
This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given one or a few template photos.
Modern face recognition systems approach the problem of one-shot learning via face recognition by learning a rich low-dimensional feature representation, called a face embedding, that can be calculated for faces easily and compared for verification and identification tasks.
Historically, embeddings were learned for one-shot learning problems using a Siamese network. The training of Siamese networks with comparative loss functions resulted in better performance, later leading to the triplet loss function used in the FaceNet system by Google that achieved then state-of-the-art results on benchmark face recognition tasks.
In this post, you will discover the challenge of one-shot learning in face recognition and how comparative and triplet loss functions can be used to learn high-quality face embeddings.
After reading this post, you will know:
- One-shot learning are classification tasks where many
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