Facial liveness detection with a mobile device

A computing 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 system 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, the at least one convolutional neural network being trained using at least one feedback network configured to improve performance of the at least one convolutional neural network in classifying images from multiple domains at least in part by generating, during training, one or more domain classifier scores representing one or more probabilities of an input training data example belonging to one or more domains.

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, wherein the deep learning model comprises at least one residual network, and wherein training the deep learning model comprises using at least one feedback network to improve performance of the at least one residual network in classifying images from multiple domains at least in part by generating, during training, one or more domain classifier scores representing one or more probabilities of an input training data example belonging to one or more domains.

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, wherein:
• the deep learning model comprises at least one residual network,
• training the deep learning model comprises using at least one feedback network to improve performance of the at least one residual network in classifying images from multiple domains at least in part by generating, during training, one or more domain classifier scores representing one or more probabilities of an input training data example belonging to one or more domains, and
• the at least one feedback network comprises a domain discriminator network configured to produce a domain classification loss vector, wherein during training:
• the domain discriminator network minimizes the domain classification loss vector;
• and
• the at least one residual network maximizes the domain classification loss vector until an optimization between the domain discriminator network and the at least one residual network is reached.