System for hand pose detection

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 processor operatively connected to the depth camera, the output device, and the memory, the processor being configured to:

receive depth map data of the hand of the user from the depth camera;

generate, using the first neural network, a first plurality of activation features base at least in part on the depth map data;

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 using nearest neighbor identification;

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.

3. The system of claim 2 , wherein the memory further stores a recommendation engine, and wherein the processor is further configured to:

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 first finger using nearest neighbor identification; 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.

4. The system of claim 3 , wherein the processor is further configured to:

identify a plurality of neural networks stored in the memory based upon the first plurality of hand pose parameters, each neural network in the plurality of neural networks being trained to classify features corresponding to joint angles of one of a second finger, a third finger, a fourth finger, and a fifth finger of the hand;

provide the depth map data of the hand of the user to each neural network in the plurality of neural networks to generate a plurality of activation features for each of the plurality of neural networks, each plurality of activation features corresponding to joint angles of one of the second, third, fourth, and fifth fingers in the depth map data.

5. The system of claim 4 , wherein the processor is further configured to:

perform a plurality of searches in the predetermined plurality of activation features stored in the database based on the plurality of activation features for each of the second, the third, the fourth, and the fifth fingers to identify additional pluralities of hand pose parameters for the second, the third, the fourth, and the fifth fingers; and

generate the hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters, the second plurality of hand pose parameters, and the additional pluralities of hand pose parameters for the second, the third, the fourth, and the fifth fingers.

6. The system of claim 5 , wherein the processor is further configured to:

perform each of the plurality of searches as a spatial search of the predetermined plurality of activation features.

7. The system of claim 5 , wherein the processor is further configured to:

perform each of the plurality of searches as a spatial-temporal search of another plurality of activation features.

8. The system of claim 2 , wherein the processor is further configured to:

perform a second search in the predetermined plurality of activation features stored in the database to identify a second plurality of hand pose parameters for the first finger using nearest neighbor identification; 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.

9. The system of claim 8 , wherein the processor is further configured to:

identify a predetermined subset of a range of joint angles for the wrist that includes the joint angle of the wrist in the first plurality of hand pose parameters; and

identify the second neural network based on a predetermined relationship between the predetermined subset of the range and the plurality of neural networks stored in the memory.

10. The system of claim 2 , wherein the processor is further configured to:

identify a predetermined subset of a range of joint angles for the wrist that includes the joint angle of the wrist in the first plurality of hand pose parameters; and

identify the second neural network based on a predetermined relationship between the predetermined subset of the range and the plurality of neural networks stored in the memory.

11. The system of claim 1 , wherein the processor is further configured to:

perform the first search as a spatial search of the predetermined plurality of activation features in the database to identify the first plurality of hand pose parameters.

12. The system of claim 1 , wherein the processor is further configured to:

perform the first search as a spatial-temporal search of another plurality of activation features and associated hand pose parameters in the database corresponding to at least one previous depth map generated by the depth camera within a predetermined time period of generation of the depth map data to identify the first plurality of hand pose parameters.