Meta-learning for facial recognition

A computer-implemented method for training a facial recognition system, the method comprising:

• selecting an image with an individual to identify;
• inputting the selected image into a trained facial recognition system implemented using a meta-model based on a neural network trained using multiple classifiers for identifying individuals by classifying overall facial images of the individuals, wherein each of the classifiers is trained using a single facial image of one of the individuals, wherein training the facial recognition system includes:
• generating the meta-model based on the neural network, wherein the neural network is trained using a first classifier trained using one facial image for a first individual to learn an identity for the first individual,
• updating the meta-model by validating the meta-model to update for error in the meta-model, the error based on an output identity by the meta-model using a second facial image for the first individual input into the meta-model, and
• testing the meta-model to fine-tune the meta-model by for updating further error;
• and
• outputting an identity for the individual in the image using the trained facial recognition system.