How to Perform Face Recognition With VGGFace2 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.

Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets. One example of a state-of-the-art model is the VGGFace and VGGFace2 model developed by researchers at the Visual Geometry Group at Oxford.

Although the model can be challenging to implement and resource intensive to train, it can be easily used in standard deep learning libraries such as Keras through the use of freely available pre-trained models and third-party open source libraries.

In this tutorial, you will discover how to develop face recognition systems for face identification and verification using the VGGFace2 deep learning model.

After completing this tutorial, you will know:

• About the VGGFace and VGGFace2 models for face recognition and how to install the keras_vggface library to make use of these models in Python with Keras.
• How to develop a face identification system to predict the name of celebrities in given photographs.
• How to develop a face verification system to confirm the identity of a person given a photograph of
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