Method and system for hand pose detection
A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.
1. A system for computer human interaction comprising:
a depth camera configured to generate depth map data of a hand of a user;
an output device;
a memory storing at least a first neural network, and a recommendation engine; and
a processor operatively connected to the depth camera, the output device, and the memory, the processor being configured to:
receive depth map data of a hand of a user from the depth camera;
generate, using the first neural network, a first plurality of activation features based at least in part on the depth map data;
use the recommendation engine to perform a first search in a predetermined plurality of activation features stored in a database of the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features;
generate a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters; and
generate an output with the output device in response to input from the user based at least in part on the hand pose model.
2. The system of claim 1 , wherein the processor is further configured to:
identify a second neural network stored in the memory based upon the first plurality of hand pose parameters, the second neural network being one neural network in a plurality of neural networks stored in the memory trained to classify features corresponding to joint angles of a first finger of the hand; and
generate a second plurality of activation features using the depth map data of the hand of the user and the second neural network;
perform a second search, using the recommendation engine, in the predetermined plurality of activation features stored in the database to identify a second plurality of hand pose parameters for the one finger associated with predetermined activation features in the database that are nearest neighbors to the second plurality of activation features; and
generate the hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and the second plurality of hand pose parameters.