| CPC G06V 10/82 (2022.01) [G06V 40/168 (2022.01); G06V 40/172 (2022.01); G06V 40/45 (2022.01)] | 20 Claims |
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1. A system, comprising:
a computer that includes a processor and a memory, the memory including instructions executable by the processor to provide output from a selected biometric analysis task that is one of a plurality of biometric analysis tasks, based on an image provided from an image sensor;
wherein the selected biometric analysis task is performed in a deep neural network that includes a common feature extraction neural network, a plurality of biometric task-specific neural networks, a segmentation neural network, a landmark mesh neural network, a plurality of soft target segmentation neural networks, and a plurality of expert pooling neural networks that perform the plurality of biometric analysis tasks by:
inputting the image to the common feature extraction network to determine latent variables;
inputting the latent variables to the plurality of biometric task-specific neural networks including emotion detection and head pose to determine a plurality of first biometric analysis task outputs;
inputting the latent variables to a landmark mesh neural network to determine a landmark mesh that includes polygons based on landmark locations that indicate features of a human face;
inputting the landmark mesh and the first biometric task outputs to a plurality of expert pooling neural networks to determine a plurality of second biometric task outputs; and
training the deep neural network by:
determining one or more first loss functions based on the plurality of second biometric task outputs;
combining the first loss functions to determine a joint loss function that gives more weight to loss functions for biometric tasks that include larger training datasets than biometric tasks that include smaller training datasets; and
backpropagating the one or more first loss function and the joint loss function back through the deep neural network.
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