How to Develop a Face Recognition System Using FaceNet in Keras
Last Updated on August 24, 2020
Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face.
FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to multiple third-party open source implementations of the model and the availability of pre-trained models.
The FaceNet system can be used to extract high-quality features from faces, called face embeddings, that can then be used to train a face identification system.
In this tutorial, you will discover how to develop a face detection system using FaceNet and an SVM classifier to identify people from photographs.
After completing this tutorial, you will know:
- About the FaceNet face recognition system developed by Google and open source implementations and pre-trained models.
- How to prepare a face detection dataset including first extracting faces via a face detection system and then extracting face features via face embeddings.
- How to fit, evaluate, and demonstrate an SVM model to predict identities from faces embeddings.
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