Facial liveness detection with a mobile device

A computing system, the system comprising:

• a camera;
• a server;
• at least one processor;
• and
• at least one non-transitory computer-readable medium comprising instructions which, when executed by the at least one processor, cause the computing device to perform a method of:
• using the at least one processor to perform:
• accessing, from the server, a plurality of images comprising a face obtained by the camera;
• providing the plurality of images to a trained deep learning model to obtain output indicating one or more likelihoods that the plurality of images comprise images of a live user and one or more likelihoods that the plurality of images comprise images of a spoof attack;
• and
• identifying the plurality of images as comprising at least one of a live user and a spoof attack based on the output obtained from the trained deep learning model,
• wherein the trained deep learning model comprises at least one convolutional neural network and at least one feedback network.

A method of training a deep learning model for identifying facial spoofing, the method comprising:

• using at least one computer hardware processor to perform:
• accessing training data obtained by extracting facial feature locations from training images;
• and
• training the deep learning model using the training data.