US 12,450,930 B2
Biometric task network
Ali Hassani, Ann Arbor, MI (US); Hafiz Malik, Canton, MI (US); Zaid El Shair, Westland, MI (US); John Robert Van Wiemeersch, Novi, MI (US); and Justin Miller, Berkley, MI (US)
Assigned to Ford Global Technologies, LLC, Dearborn, MI (US)
Filed by Ford Global Technologies, LLC, Dearborn, MI (US)
Filed on Apr. 27, 2022, as Appl. No. 17/730,294.
Claims priority of provisional application 63/310,401, filed on Feb. 15, 2022.
Prior Publication US 2023/0260301 A1, Aug. 17, 2023
Int. Cl. G06V 30/18 (2022.01)
CPC G06V 30/18019 (2022.01) 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a computer that includes a processor and a memory, the memory including instructions executable by the processor to:
receive an image at a common feature extraction network to determine skip connected latent variables;
receive the skip connected latent variables at a plurality of biometric task-specific neural networks to determine a plurality of first outputs;
receive the first outputs and the skip connected latent variables at a plurality of expert pooling neural networks to determine one or more biometric analysis task outputs; and
wherein the selected one or more biometric analysis task outputs are determined in a deep neural network that includes the common feature extraction neural network, the plurality of biometric task-specific neural networks and the plurality of expert pooling networks that are trained based on a joint loss function which compensates for differences in sizes of training datasets for the biometric task-specific neural networks.